cuGraph API Reference

Structure

Graph

class cugraph.structure.graph.Graph(m_graph=None, edge_attr=None, symmetrized=False, bipartite=False, multi=False, dynamic=False)

Methods

add_internal_vertex_id(df, …[, drop, …])	Given a DataFrame containing external vertex ids in the identified columns, return a DataFrame containing the internal vertex ids as the specified column name.
add_nodes_from(nodes[, bipartite, multipartite])	Add nodes information to the Graph.
clear()	Empty this graph.
compute_local_data(by[, load_balance])	Compute the local edges, vertices and offsets for a distributed graph stored as a dask-cudf dataframe and initialize the communicator.
compute_renumber_edge_list([transposed])	Compute a renumbered edge list
degree([vertex_subset])	Compute vertex degree, which is the total number of edges incident to a vertex (both in and out edges).
degrees([vertex_subset])	Compute vertex in-degree and out-degree.
delete_adj_list()	Delete the adjacency list.
delete_edge_list()	Delete the edge list.
edges()	Returns all the edges in the graph as a cudf.DataFrame containing sources and destinations.
from_cudf_adjlist(offset_col, index_col[, …])	Initialize a graph from the adjacency list.
from_cudf_edgelist(input_df[, source, …])	Initialize a graph from the edge list.
from_dask_cudf_edgelist(input_ddf[, source, …])	Initializes the distributed graph from the dask_cudf.DataFrame edgelist.
from_numpy_array(np_array[, nodes])	Initializes the graph from numpy array containing adjacency matrix.
from_numpy_matrix(np_matrix)	Initializes the graph from numpy matrix containing adjacency matrix.
from_pandas_adjacency(pdf)	Initializes the graph from pandas adjacency matrix
from_pandas_edgelist(pdf[, source, …])	Initialize a graph from the edge list.
get_two_hop_neighbors()	Compute vertex pairs that are two hops apart.
has_edge(u, v)	Returns True if the graph contains the edge (u,v).
has_node(n)	Returns True if the graph contains the node n.
in_degree([vertex_subset])	Compute vertex in-degree.
is_bipartite()	Checks if Graph is bipartite.
is_multipartite()	Checks if Graph is multipartite.
lookup_internal_vertex_id(df[, column_name])	Given a DataFrame containing external vertex ids in the identified columns, or a Series containing external vertex ids, return a Series with the internal vertex ids.
nodes()	Returns all the nodes in the graph as a cudf.Series
number_of_edges([directed_edges])	Get the number of edges in the graph.
number_of_nodes()	An alias of number_of_vertices().
number_of_vertices()	Get the number of nodes in the graph.
out_degree([vertex_subset])	Compute vertex out-degree.
sets()	Returns the bipartite set of nodes.
to_directed()	Return a directed representation of the graph.
to_numpy_array()	Returns the graph adjacency matrix as a NumPy array.
to_numpy_matrix()	Returns the graph adjacency matrix as a NumPy matrix.
to_pandas_adjacency()	Returns the graph adjacency matrix as a Pandas DataFrame.
to_pandas_edgelist([source, destination])	Returns the graph edge list as a Pandas DataFrame.
to_undirected()	Return an undirected copy of the graph.
unrenumber(df, column_name[, preserve_order])	Given a DataFrame containing internal vertex ids in the identified column, replace this with external vertex ids.
view_adj_list()	Display the adjacency list.
view_edge_list()	Display the edge list.
view_transposed_adj_list()	Display the transposed adjacency list.

AdjList
EdgeList
enable_batch
is_directed
neighbors
transposedAdjList

class AdjList(offsets, indices, value=None)

class EdgeList(*args)

add_internal_vertex_id(df, internal_column_name, external_column_name, drop=True, preserve_order=False)

Given a DataFrame containing external vertex ids in the identified columns, return a DataFrame containing the internal vertex ids as the specified column name. Optionally drop the external vertex id columns. Optionally preserve the order of the original DataFrame.

Parameters

df: cudf.DataFrame or dask_cudf.DataFrame

A DataFrame containing external vertex identifiers that will be converted into internal vertex identifiers.

internal_column_name: string

Name of column to contain the internal vertex id

external_column_name: string or list of strings

Name of the column(s) containing the external vertex ids

drop: (optional) bool, defaults to True

Drop the external columns from the returned DataFrame

preserve_order: (optional) bool, defaults to False

Preserve the order of the data frame (requires an extra sort)

Returns

dfcudf.DataFrame or dask_cudf.DataFrame

Original DataFrame with new column containing internal vertex id

add_nodes_from(nodes, bipartite=None, multipartite=None)

Add nodes information to the Graph.

Parameters

nodeslist or cudf.Series

The nodes of the graph to be stored. If bipartite and multipartite arguments are not passed, the nodes are considered to be a list of all the nodes present in the Graph.

bipartitestr

Sets the Graph as bipartite. The nodes are stored as a set of nodes of the partition named as bipartite argument.

multipartitestr

Sets the Graph as multipartite. The nodes are stored as a set of nodes of the partition named as multipartite argument.

clear()

Empty this graph. This function is added for NetworkX compatibility.

compute_local_data(by, load_balance=True)

Compute the local edges, vertices and offsets for a distributed graph stored as a dask-cudf dataframe and initialize the communicator. Performs global sorting and load_balancing.

Parameters

bystr

by argument is the column by which we want to sort and partition. It should be the source column name for generating CSR format and destination column name for generating CSC format.

load_balancebool

Set as True to perform load_balancing after global sorting of dask-cudf DataFrame. This ensures that the data is uniformly distributed among multiple GPUs to avoid over-loading.

compute_renumber_edge_list(transposed=False)

Compute a renumbered edge list

This function works in the MNMG pipeline and will transform the input dask_cudf.DataFrame into a renumbered edge list in the prescribed direction.

This function will be called by the algorithms to ensure that the graph is renumbered properly. The graph object will cache the most recent renumbering attempt. For benchmarking purposes, this function can be called prior to calling a graph algorithm so we can measure the cost of computing the renumbering separately from the cost of executing the algorithm.

When creating a CSR-like structure, set transposed to False. When creating a CSC-like structure, set transposed to True.

Parameters

transposed(optional) bool

If True, renumber with the intent to make a CSC-like structure. If False, renumber with the intent to make a CSR-like structure. Defaults to False.

degree(vertex_subset=None)

Compute vertex degree, which is the total number of edges incident to a vertex (both in and out edges). By default, this method computes degrees for the entire set of vertices. If vertex_subset is provided, then this method optionally filters out all but those listed in vertex_subset.

Parameters

vertex_subsetcudf.Series or iterable container, optional

a container of vertices for displaying corresponding degree. If not set, degrees are computed for the entire set of vertices.

Returns

dfcudf.DataFrame

GPU DataFrame of size N (the default) or the size of the given vertices (vertex_subset) containing the degree. The ordering is relative to the adjacency list, or that given by the specified vertex_subset.

df[‘vertex’]cudf.Series

The vertex IDs (will be identical to vertex_subset if specified).

df[‘degree’]cudf.Series

The computed degree of the corresponding vertex.

Examples

>>> M = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>>                   dtype=['int32', 'int32', 'float32'], header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(M, '0', '1')
>>> all_df = G.degree()
>>> subset_df = G.degree([0,9,12])

degrees(vertex_subset=None)

Compute vertex in-degree and out-degree. By default, this method computes vertex degrees for the entire set of vertices. If vertex_subset is provided, this method optionally filters out all but those listed in vertex_subset.

Parameters

vertex_subsetcudf.Series or iterable container, optional

A container of vertices for displaying corresponding degree. If not set, degrees are computed for the entire set of vertices.

Returns

dfcudf.DataFrame

GPU DataFrame of size N (the default) or the size of the given vertices (vertex_subset) containing the degrees. The ordering is relative to the adjacency list, or that given by the specified vertex_subset.

df[‘vertex’]cudf.Series

The vertex IDs (will be identical to vertex_subset if specified).

df[‘in_degree’]cudf.Series

The in-degree of the vertex.

df[‘out_degree’]cudf.Series

The out-degree of the vertex.

Examples

>>> M = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>>                   dtype=['int32', 'int32', 'float32'], header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(M, '0', '1')
>>> df = G.degrees([0,9,12])

delete_adj_list()

Delete the adjacency list.

delete_edge_list()

Delete the edge list.

edges()

Returns all the edges in the graph as a cudf.DataFrame containing sources and destinations. It does not return the edge weights. For viewing edges with weights use view_edge_list()

enable_batch()

from_cudf_adjlist(offset_col, index_col, value_col=None)

Initialize a graph from the adjacency list. It is an error to call this method on an initialized Graph object. The passed offset_col and index_col arguments wrap gdf_column objects that represent a graph using the adjacency list format. If value_col is None, an unweighted graph is created. If value_col is not None, a weighted graph is created. Undirected edges must be stored as directed edges in both directions.

Parameters

offset_colcudf.Series

This cudf.Series wraps a gdf_column of size V + 1 (V: number of vertices). The gdf column contains the offsets for the vertices in this graph. Offsets must be in the range [0, E] (E: number of edges).

index_colcudf.Series

This cudf.Series wraps a gdf_column of size E (E: number of edges). The gdf column contains the destination index for each edge. Destination indices must be in the range [0, V) (V: number of vertices).

value_colcudf.Series, optional

This pointer can be None. If not, this cudf.Series wraps a gdf_column of size E (E: number of edges). The gdf column contains the weight value for each edge. The expected type of the gdf_column element is floating point number.

Examples

>>> gdf = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>>                   dtype=['int32', 'int32', 'float32'], header=None)
>>> M = gdf.to_pandas()
>>> M = scipy.sparse.coo_matrix((M['2'],(M['0'],M['1'])))
>>> M = M.tocsr()
>>> offsets = cudf.Series(M.indptr)
>>> indices = cudf.Series(M.indices)
>>> G = cugraph.Graph()
>>> G.from_cudf_adjlist(offsets, indices, None)

from_cudf_edgelist(input_df, source='source', destination='destination', edge_attr=None, renumber=True)

Initialize a graph from the edge list. It is an error to call this method on an initialized Graph object. The passed input_df argument wraps gdf_column objects that represent a graph using the edge list format. source argument is source column name and destination argument is destination column name.

By default, renumbering is enabled to map the source and destination vertices into an index in the range [0, V) where V is the number of vertices. If the input vertices are a single column of integers in the range [0, V), renumbering can be disabled and the original external vertex ids will be used.

If weights are present, edge_attr argument is the weights column name.

Parameters

input_dfcudf.DataFrame or dask_cudf.DataFrame

A DataFrame that contains edge information If a dask_cudf.DataFrame is passed it will be reinterpreted as a cudf.DataFrame. For the distributed path please use from_dask_cudf_edgelist.

sourcestr or array-like

source column name or array of column names

destinationstr or array-like

destination column name or array of column names

edge_attrstr or None

the weights column name. Default is None

renumberbool

Indicate whether or not to renumber the source and destination vertex IDs. Default is True.

Examples

>>> df = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>>                   dtype=['int32', 'int32', 'float32'], header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(df, source='0', destination='1',
                         edge_attr='2', renumber=False)

from_dask_cudf_edgelist(input_ddf, source='source', destination='destination', edge_attr=None, renumber=True)

Initializes the distributed graph from the dask_cudf.DataFrame edgelist. Undirected Graphs are not currently supported.

By default, renumbering is enabled to map the source and destination vertices into an index in the range [0, V) where V is the number of vertices. If the input vertices are a single column of integers in the range [0, V), renumbering can be disabled and the original external vertex ids will be used.

Note that the graph object will store a reference to the dask_cudf.DataFrame provided.

Parameters

input_ddfdask_cudf.DataFrame

The edgelist as a dask_cudf.DataFrame

sourcestr or array-like

source column name or array of column names

destinationstr

destination column name or array of column names

edge_attrstr

weights column name.

renumberbool

If source and destination indices are not in range 0 to V where V is number of vertices, renumber argument should be True.

from_numpy_array(np_array, nodes=None)

Initializes the graph from numpy array containing adjacency matrix.

from_numpy_matrix(np_matrix)

Initializes the graph from numpy matrix containing adjacency matrix.

from_pandas_adjacency(pdf)

Initializes the graph from pandas adjacency matrix

from_pandas_edgelist(pdf, source='source', destination='destination', edge_attr=None, renumber=True)

Initialize a graph from the edge list. It is an error to call this method on an initialized Graph object. Source argument is source column name and destination argument is destination column name.

By default, renumbering is enabled to map the source and destination vertices into an index in the range [0, V) where V is the number of vertices. If the input vertices are a single column of integers in the range [0, V), renumbering can be disabled and the original external vertex ids will be used.

If weights are present, edge_attr argument is the weights column name.

Parameters

input_dfpandas.DataFrame

A DataFrame that contains edge information

sourcestr or array-like

source column name or array of column names

destinationstr or array-like

destination column name or array of column names

edge_attrstr or None

the weights column name. Default is None

renumberbool

Indicate whether or not to renumber the source and destination vertex IDs. Default is True.

Examples

>>> df = pandas.read_csv('datasets/karate.csv', delimiter=' ',
>>>                   dtype=['int32', 'int32', 'float32'], header=None)
>>> G = cugraph.Graph()
>>> G.from_pandas_edgelist(df, source='0', destination='1',
                         edge_attr='2', renumber=False)

get_two_hop_neighbors()

Compute vertex pairs that are two hops apart. The resulting pairs are sorted before returning.

Returns

dfcudf.DataFrame

df[first]cudf.Series

the first vertex id of a pair, if an external vertex id is defined by only one column

df[second]cudf.Series

the second vertex id of a pair, if an external vertex id is defined by only one column

has_edge(u, v)

Returns True if the graph contains the edge (u,v).

has_node(n)

Returns True if the graph contains the node n.

in_degree(vertex_subset=None)

Compute vertex in-degree. Vertex in-degree is the number of edges pointing into the vertex. By default, this method computes vertex degrees for the entire set of vertices. If vertex_subset is provided, this method optionally filters out all but those listed in vertex_subset.

Parameters

vertex_subsetcudf.Series or iterable container, optional

A container of vertices for displaying corresponding in-degree. If not set, degrees are computed for the entire set of vertices.

Returns

dfcudf.DataFrame

GPU DataFrame of size N (the default) or the size of the given vertices (vertex_subset) containing the in_degree. The ordering is relative to the adjacency list, or that given by the specified vertex_subset.

df[vertex]cudf.Series

The vertex IDs (will be identical to vertex_subset if specified).

df[degree]cudf.Series

The computed in-degree of the corresponding vertex.

Examples

>>> M = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>>                   dtype=['int32', 'int32', 'float32'], header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(M, '0', '1')
>>> df = G.in_degree([0,9,12])

is_bipartite()

Checks if Graph is bipartite. This solely relies on the user call of add_nodes_from with the bipartite parameter. This does not parse the graph to check if it is bipartite.

is_directed()

is_multipartite()

Checks if Graph is multipartite. This solely relies on the user call of add_nodes_from with the partition parameter. This does not parse the graph to check if it is multipartite.

lookup_internal_vertex_id(df, column_name=None)

Given a DataFrame containing external vertex ids in the identified columns, or a Series containing external vertex ids, return a Series with the internal vertex ids.

Note that this function does not guarantee order in single GPU mode, and does not guarantee order or partitioning in multi-GPU mode.

Parameters

df: cudf.DataFrame, cudf.Series, dask_cudf.DataFrame, dask_cudf.Series

A DataFrame containing external vertex identifiers that will be converted into internal vertex identifiers.

column_name: (optional) string

Name of the column containing the external vertex ids

Returns

seriescudf.Series or dask_cudf.Series

The internal vertex identifiers

neighbors(n)

nodes()

Returns all the nodes in the graph as a cudf.Series

number_of_edges(directed_edges=False)

Get the number of edges in the graph.

number_of_nodes()

An alias of number_of_vertices(). This function is added for NetworkX compatibility.

number_of_vertices()

Get the number of nodes in the graph.

out_degree(vertex_subset=None)

Compute vertex out-degree. Vertex out-degree is the number of edges pointing out from the vertex. By default, this method computes vertex degrees for the entire set of vertices. If vertex_subset is provided, this method optionally filters out all but those listed in vertex_subset.

Parameters

vertex_subsetcudf.Series or iterable container, optional

A container of vertices for displaying corresponding out-degree. If not set, degrees are computed for the entire set of vertices.

Returns

dfcudf.DataFrame

GPU DataFrame of size N (the default) or the size of the given vertices (vertex_subset) containing the out_degree. The ordering is relative to the adjacency list, or that given by the specified vertex_subset.

df[vertex]cudf.Series

The vertex IDs (will be identical to vertex_subset if specified).

df[degree]cudf.Series

The computed out-degree of the corresponding vertex.

Examples

>>> M = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>>                   dtype=['int32', 'int32', 'float32'], header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(M, '0', '1')
>>> df = G.out_degree([0,9,12])

sets()

Returns the bipartite set of nodes. This solely relies on the user’s call of add_nodes_from with the bipartite parameter. This does not parse the graph to compute bipartite sets. If bipartite argument was not provided during add_nodes_from(), it raise an exception that the graph is not bipartite.

to_directed()

Return a directed representation of the graph. This function sets the type of graph as DiGraph() and returns the directed view.

Returns

GDiGraph

A directed graph with the same nodes, and each edge (u,v,weights) replaced by two directed edges (u,v,weights) and (v,u,weights).

Examples

>>> M = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>>                   dtype=['int32', 'int32', 'float32'], header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(M, '0', '1')
>>> DiG = G.to_directed()

to_numpy_array()

Returns the graph adjacency matrix as a NumPy array.

to_numpy_matrix()

Returns the graph adjacency matrix as a NumPy matrix.

to_pandas_adjacency()

Returns the graph adjacency matrix as a Pandas DataFrame.

to_pandas_edgelist(source='source', destination='destination')

Returns the graph edge list as a Pandas DataFrame.

Parameters

sourcestr or array-like

source column name or array of column names

destinationstr or array-like

destination column name or array of column names

Returns

dfpandas.DataFrame

to_undirected()

Return an undirected copy of the graph.

Returns

GGraph

A undirected graph with the same nodes, and each directed edge (u,v,weights) replaced by an undirected edge (u,v,weights).

Examples

>>> M = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>>                   dtype=['int32', 'int32', 'float32'], header=None)
>>> DiG = cugraph.DiGraph()
>>> DiG.from_cudf_edgelist(M, '0', '1')
>>> G = DiG.to_undirected()

class transposedAdjList(offsets, indices, value=None)

unrenumber(df, column_name, preserve_order=False)

Given a DataFrame containing internal vertex ids in the identified column, replace this with external vertex ids. If the renumbering is from a single column, the output dataframe will use the same name for the external vertex identifiers. If the renumbering is from a multi-column input, the output columns will be labeled 0 through n-1 with a suffix of _column_name.

Note that this function does not guarantee order in single GPU mode, and does not guarantee order or partitioning in multi-GPU mode. If you wish to preserve ordering, add an index column to df and sort the return by that index column.

Parameters

df: cudf.DataFrame or dask_cudf.DataFrame

A DataFrame containing internal vertex identifiers that will be converted into external vertex identifiers.

column_name: string

Name of the column containing the internal vertex id.

preserve_order: (optional) bool

If True, preserve the order of the rows in the output DataFrame to match the input DataFrame

Returns

dfcudf.DataFrame or dask_cudf.DataFrame

The original DataFrame columns exist unmodified. The external vertex identifiers are added to the DataFrame, the internal vertex identifier column is removed from the dataframe.

view_adj_list()

Display the adjacency list. Compute it if needed.

Returns

offset_colcudf.Series

This cudf.Series wraps a gdf_column of size V + 1 (V: number of vertices). The gdf column contains the offsets for the vertices in this graph. Offsets are in the range [0, E] (E: number of edges).

index_colcudf.Series

This cudf.Series wraps a gdf_column of size E (E: number of edges). The gdf column contains the destination index for each edge. Destination indices are in the range [0, V) (V: number of vertices).

value_colcudf.Series or None

This pointer is None for unweighted graphs. For weighted graphs, this cudf.Series wraps a gdf_column of size E (E: number of edges). The gdf column contains the weight value for each edge. The expected type of the gdf_column element is floating point number.

view_edge_list()

Display the edge list. Compute it if needed.

NOTE: If the graph is of type Graph() then the displayed undirected edges are the same as displayed by networkx Graph(), but the direction could be different i.e. an edge displayed by cugraph as (src, dst) could be displayed as (dst, src) by networkx.

cugraph.Graph stores symmetrized edgelist internally. For displaying undirected edgelist for a Graph the upper trianglar matrix of the symmetrized edgelist is returned.

networkx.Graph renumbers the input and stores the upper triangle of this renumbered input. Since the internal renumbering of networx and cugraph is different, the upper triangular matrix of networkx renumbered input may not be the same as cugraph’s upper trianglar matrix of the symmetrized edgelist. Hence the displayed source and destination pairs in both will represent the same edge but node values could be swapped.

Returns

dfcudf.DataFrame

This cudf.DataFrame wraps source, destination and weight

df[src]cudf.Series

contains the source index for each edge

df[dst]cudf.Series

contains the destination index for each edge

df[weight]cusd.Series

Column is only present for weighted Graph, then containing the weight value for each edge

view_transposed_adj_list()

Display the transposed adjacency list. Compute it if needed.

Returns

offset_colcudf.Series

This cudf.Series wraps a gdf_column of size V + 1 (V: number of vertices). The gdf column contains the offsets for the vertices in this graph. Offsets are in the range [0, E] (E: number of edges).

index_colcudf.Series

This cudf.Series wraps a gdf_column of size E (E: number of edges). The gdf column contains the destination index for each edge. Destination indices are in the range [0, V) (V: number of vertices).

value_colcudf.Series or None

This pointer is None for unweighted graphs. For weighted graphs, this cudf.Series wraps a gdf_column of size E (E: number of edges). The gdf column contains the weight value for each edge. The expected type of the gdf_column element is floating point number.

Symmetrize

cugraph.structure.symmetrize.symmetrize(source_col, dest_col, value_col=None)

Take a COO set of source destination pairs along with associated values stored in a single GPU or distributed create a new COO set of source destination pairs along with values where all edges exist in both directions.

Return from this call will be a COO stored as two cudf Series or dask_cudf.Series -the symmetrized source column and the symmetrized dest column, along with an optional cudf Series containing the associated values (only if the values are passed in).

Parameters

source_colcudf.Series or dask_cudf.Series

This cudf.Series wraps a gdf_column of size E (E: number of edges). The gdf column contains the source index for each edge. Source indices must be an integer type.

dest_colcudf.Series or dask_cudf.Series

This cudf.Series wraps a gdf_column of size E (E: number of edges). The gdf column contains the destination index for each edge. Destination indices must be an integer type.

value_colcudf.Series or dask_cudf.Series (optional)

This cudf.Series wraps a gdf_column of size E (E: number of edges). The gdf column contains values associated with this edge. For this function the values can be any type, they are not examined, just copied.

Examples

>>> M = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>>                   dtype=['int32', 'int32', 'float32'], header=None)
>>> sources = cudf.Series(M['0'])
>>> destinations = cudf.Series(M['1'])
>>> values = cudf.Series(M['2'])
>>> src, dst, val = cugraph.symmetrize(sources, destinations, values)

cugraph.structure.symmetrize.symmetrize_ddf(df, src_name, dst_name, weight_name=None)

Take a COO stored in a distributed DataFrame, and the column names of the source and destination columns and create a new data frame using the same column names that symmetrize the graph so that all edges appear in both directions.

Note that if other columns exist in the data frame (e.g. edge weights) the other columns will also be replicated. That is, if (u,v,data) represents the source value (u), destination value (v) and some set of other columns (data) in the input data, then the output data will contain both (u,v,data) and (v,u,data) with matching data.

If (u,v,data1) and (v,u,data2) exist in the input data where data1 != data2 then this code will arbitrarily pick the smaller data element to keep, if this is not desired then the caller should should correct the data prior to calling symmetrize.

Parameters

dfdask_cudf.DataFrame

Input data frame containing COO. Columns should contain source ids, destination ids and any properties associated with the edges.

src_namestring

Name of the column in the data frame containing the source ids

dst_namestring

Name of the column in the data frame containing the destination ids

Examples

>>> M = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>>                   dtype=['int32', 'int32', 'float32'], header=None)
>>> sym_df = cugraph.symmetrize(M, '0', '1')

cugraph.structure.symmetrize.symmetrize_df(df, src_name, dst_name)

Take a COO stored in a DataFrame, along with the column names of the source and destination columns and create a new data frame using the same column names that symmetrize the graph so that all edges appear in both directions. Note that if other columns exist in the data frame (e.g. edge weights) the other columns will also be replicated. That is, if (u,v,data) represents the source value (u), destination value (v) and some set of other columns (data) in the input data, then the output data will contain both (u,v,data) and (v,u,data) with matching data. If (u,v,data1) and (v,u,data2) exist in the input data where data1 != data2 then this code will arbitrarily pick the smaller data element to keep, if this is not desired then the caller should should correct the data prior to calling symmetrize. Parameters ———- df : cudf.DataFrame

Input data frame containing COO. Columns should contain source ids, destination ids and any properties associated with the edges.

src_namestring

Name of the column in the data frame containing the source ids

dst_namestring

Name of the column in the data frame containing the destination ids

>>> import cugraph.dask as dcg
>>> Comms.initialize()
>>> chunksize = dcg.get_chunksize(input_data_path)
>>> ddf = dask_cudf.read_csv(input_data_path, chunksize=chunksize,
                             delimiter=' ',
                             names=['src', 'dst', 'weight'],
                             dtype=['int32', 'int32', 'float32'])
>>> sym_ddf = cugraph.symmetrize_ddf(ddf, "src", "dst", "weight")
>>> Comms.destroy()

Conversion from Other Formats

cugraph.structure.convert_matrix.from_adjlist(offsets, indices, values=None, create_using=<class 'cugraph.structure.graph.Graph'>)

Initializes the graph from cuDF or Pandas Series representing adjacency matrix CSR data and returns a new cugraph.Graph object if ‘create_using’ is set to cugraph.Graph (the default), or cugraph.DiGraph if ‘create_using’ is set to cugraph.DiGraph.

Parameters

offsetscudf.Series, pandas.Series

The offsets of a CSR adjacency matrix.

indicescudf.Series, pandas.Series

The indices of a CSR adjacency matrix.

valuescudf.Series, pandas.Series, or None (default), optional

The values in a CSR adjacency matrix, which represent edge weights in a graph. If not provided, the resulting graph is considered unweighted.

create_usingcuGraph.Graph

Specify the type of Graph to create. Default is cugraph.Graph

Examples

>>> pdf = pd.read_csv('datasets/karate.csv', delimiter=' ',
...                   dtype={0:'int32', 1:'int32', 2:'float32'},
...                   header=None)
>>> M = scipy.sparse.coo_matrix((pdf[2],(pdf[0],pdf[1])))
>>> M = M.tocsr()
>>> offsets = pd.Series(M.indptr)
>>> indices = pd.Series(M.indices)
>>> G = cugraph.from_adjlist(offsets, indices, None)

cugraph.structure.convert_matrix.from_cudf_edgelist(df, source='source', destination='destination', edge_attr=None, create_using=<class 'cugraph.structure.graph.Graph'>, renumber=True)

Return a new graph created from the edge list representaion. This function is added for NetworkX compatibility (this function is a RAPIDS version of NetworkX’s from_pandas_edge_list()). This function does not support multiple source or destination columns. But does support renumbering

Parameters

dfcudf.DataFrame

This cudf.DataFrame contains columns storing edge source vertices, destination (or target following NetworkX’s terminology) vertices, and (optional) weights.

sourcestring or integer

This is used to index the source column.

destinationstring or integer

This is used to index the destination (or target following NetworkX’s terminology) column.

edge_attrstring or integer, optional

This pointer can be None. If not, this is used to index the weight column.

create_usingcuGraph.Graph

Specify the type of Graph to create. Default is cugraph.Graph

renumberbool

If source and destination indices are not in range 0 to V where V is number of vertices, renumber argument should be True.

Examples

>>> M = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>>                   dtype=['int32', 'int32', 'float32'], header=None)
>>> G = cugraph.Graph()
>>> G = cugraph.from_cudf_edgelist(M, source='0', target='1', weight='2')

cugraph.structure.convert_matrix.from_edgelist(df, source='source', destination='destination', edge_attr=None, create_using=<class 'cugraph.structure.graph.Graph'>, renumber=True)

Return a new graph created from the edge list representaion.

Parameters

dfcudf.DataFrame, pandas.DataFrame, dask_cudf.core.DataFrame

This DataFrame contains columns storing edge source vertices, destination (or target following NetworkX’s terminology) vertices, and (optional) weights.

sourcestring or integer

This is used to index the source column.

destinationstring or integer

This is used to index the destination (or target following NetworkX’s terminology) column.

edge_attrstring or integer, optional

This pointer can be None. If not, this is used to index the weight column.

create_usingcuGraph.Graph

Specify the type of Graph to create. Default is cugraph.Graph

renumberbool

If source and destination indices are not in range 0 to V where V is number of vertices, renumber argument should be True.

Examples

>>> M = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>>                   dtype=['int32', 'int32', 'float32'], header=None)
>>> G = cugraph.Graph()
>>> G = cugraph.from_edgelist(M, source='0', destination='1',
                              edge_attr='2')

cugraph.structure.convert_matrix.from_numpy_array(A, create_using=<class 'cugraph.structure.graph.Graph'>)

Initializes the graph from numpy array containing adjacency matrix. Set create_using to cugraph.DiGraph for directed graph and cugraph.Graph for undirected Graph.

cugraph.structure.convert_matrix.from_numpy_matrix(A, create_using=<class 'cugraph.structure.graph.Graph'>)

Initializes the graph from numpy matrix containing adjacency matrix. Set create_using to cugraph.DiGraph for directed graph and cugraph.Graph for undirected Graph.

cugraph.structure.convert_matrix.from_pandas_adjacency(df, create_using=<class 'cugraph.structure.graph.Graph'>)

Initializes the graph from pandas adjacency matrix. Set create_using to cugraph.DiGraph for directed graph and cugraph.Graph for undirected Graph.

cugraph.structure.convert_matrix.from_pandas_edgelist(df, source='source', destination='destination', edge_attr=None, create_using=<class 'cugraph.structure.graph.Graph'>, renumber=True)

Initialize a graph from the edge list. It is an error to call this method on an initialized Graph object. Source argument is source column name and destination argument is destination column name.

By default, renumbering is enabled to map the source and destination vertices into an index in the range [0, V) where V is the number of vertices. If the input vertices are a single column of integers in the range [0, V), renumbering can be disabled and the original external vertex ids will be used.

If weights are present, edge_attr argument is the weights column name.

Parameters

input_dfpandas.DataFrame

A DataFrame that contains edge information

sourcestr or array-like

source column name or array of column names

destinationstr or array-like

destination column name or array of column names

edge_attrstr or None

the weights column name. Default is None

renumberbool

Indicate whether or not to renumber the source and destination vertex IDs. Default is True.

create_using: cugraph.DiGraph or cugraph.Graph

Indicate whether to create a directed or undirected graph

Returns

Gcugraph.DiGraph or cugraph.Graph

graph containing edges from the pandas edgelist

Examples

>>> df = pandas.read_csv('datasets/karate.csv', delimiter=' ',
>>>                   dtype=['int32', 'int32', 'float32'], header=None)
>>> G = cugraph.Graph()
>>> G.from_pandas_edgelist(df, source='0', destination='1',
                           edge_attr='2', renumber=False)

cugraph.structure.convert_matrix.to_numpy_array(G)

Returns the graph adjacency matrix as a NumPy array.

cugraph.structure.convert_matrix.to_numpy_matrix(G)

Returns the graph adjacency matrix as a NumPy matrix.

cugraph.structure.convert_matrix.to_pandas_adjacency(G)

Returns the graph adjacency matrix as a Pandas DataFrame. The row indices denote source and column names denote destination.

cugraph.structure.convert_matrix.to_pandas_edgelist(G, source='source', destination='destination')

Returns the graph edge list as a Pandas DataFrame.

Parameters

Gcugraph.Graph or cugraph.DiGraph

Graph containg the edgelist.

sourcestr or array-like

source column name or array of column names

destinationstr or array-like

destination column name or array of column names

Returns

——

dfpandas.DataFrame

pandas dataframe containing the edgelist as source and destination columns.

Centrality

Betweenness Centrality

cugraph.centrality.betweenness_centrality.betweenness_centrality(G, k=None, normalized=True, weight=None, endpoints=False, seed=None, result_dtype=<class 'numpy.float64'>)

Compute the betweenness centrality for all vertices of the graph G. Betweenness centrality is a measure of the number of shortest paths that pass through a vertex. A vertex with a high betweenness centrality score has more paths passing through it and is therefore believed to be more important. Rather than doing an all-pair shortest path, a sample of k starting vertices can be used.

CuGraph does not currently support the ‘endpoints’ and ‘weight’ parameters as seen in the corresponding networkX call.

Parameters

GcuGraph.Graph or networkx.Graph

The graph can be either directed (DiGraph) or undirected (Graph). Weights in the graph are ignored, the current implementation uses BFS traversals. Use weight parameter if weights need to be considered (currently not supported)

kint or list or None, optional, default=None

If k is not None, use k node samples to estimate betweenness. Higher values give better approximation If k is a list, use the content of the list for estimation: the list should contain vertices identifiers. If k is None (the default), all the vertices are used to estimate betweenness. Vertices obtained through sampling or defined as a list will be used as sources for traversals inside the algorithm.

normalizedbool, optional

Default is True. If true, the betweenness values are normalized by 2 / ((n - 1) * (n - 2)) for Graphs (undirected), and 1 / ((n - 1) * (n - 2)) for DiGraphs (directed graphs) where n is the number of nodes in G. Normalization will ensure that values are in [0, 1], this normalization scales for the highest possible value where one node is crossed by every single shortest path.

weightcudf.DataFrame, optional, default=None

Specifies the weights to be used for each edge. Should contain a mapping between edges and weights. (Not Supported)

endpointsbool, optional, default=False

If true, include the endpoints in the shortest path counts. (Not Supported)

seedoptional

if k is specified and k is an integer, use seed to initialize the random number generator. Using None as seed relies on random.seed() behavior: using current system time If k is either None or list: seed parameter is ignored

result_dtypenp.float32 or np.float64, optional, default=np.float64

Indicate the data type of the betweenness centrality scores

Returns

dfcudf.DataFrame or Dictionary if using NetworkX

GPU data frame containing two cudf.Series of size V: the vertex identifiers and the corresponding betweenness centrality values. Please note that the resulting the ‘vertex’ column might not be in ascending order. The Dictionary conatains the same two columns

df[‘vertex’]cudf.Series

Contains the vertex identifiers

df[‘betweenness_centrality’]cudf.Series

Contains the betweenness centrality of vertices

Examples

>>> gdf = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>>                   dtype=['int32', 'int32', 'float32'], header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(gdf, source='0', destination='1')
>>> bc = cugraph.betweenness_centrality(G)

cugraph.centrality.betweenness_centrality.edge_betweenness_centrality(G, k=None, normalized=True, weight=None, seed=None, result_dtype=<class 'numpy.float64'>)

Compute the edge betweenness centrality for all edges of the graph G. Betweenness centrality is a measure of the number of shortest paths that pass over an edge. An edge with a high betweenness centrality score has more paths passing over it and is therefore believed to be more important. Rather than doing an all-pair shortest path, a sample of k starting vertices can be used.

CuGraph does not currently support the ‘weight’ parameter as seen in the corresponding networkX call.

Parameters

GcuGraph.Graph or networkx.Graph

The graph can be either directed (DiGraph) or undirected (Graph). Weights in the graph are ignored, the current implementation uses BFS traversals. Use weight parameter if weights need to be considered (currently not supported)

kint or list or None, optional, default=None

If k is not None, use k node samples to estimate betweenness. Higher values give better approximation If k is a list, use the content of the list for estimation: the list should contain vertices identifiers. Vertices obtained through sampling or defined as a list will be used as sources for traversals inside the algorithm.

normalizedbool, optional

Default is True. If true, the betweenness values are normalized by 2 / (n * (n - 1)) for Graphs (undirected), and 1 / (n * (n - 1)) for DiGraphs (directed graphs) where n is the number of nodes in G. Normalization will ensure that values are in [0, 1], this normalization scales for the highest possible value where one edge is crossed by every single shortest path.

weightcudf.DataFrame, optional, default=None

Specifies the weights to be used for each edge. Should contain a mapping between edges and weights. (Not Supported)

seedoptional

if k is specified and k is an integer, use seed to initialize the random number generator. Using None as seed relies on random.seed() behavior: using current system time If k is either None or list: seed parameter is ignored

result_dtypenp.float32 or np.float64, optional, default=np.float64

Indicate the data type of the betweenness centrality scores Using double automatically switch implementation to “default”

Returns

dfcudf.DataFrame or Dictionary if using NetworkX

GPU data frame containing three cudf.Series of size E: the vertex identifiers of the sources, the vertex identifies of the destinations and the corresponding betweenness centrality values. Please note that the resulting the ‘src’, ‘dst’ column might not be in ascending order.

df[‘src’]cudf.Series

Contains the vertex identifiers of the source of each edge

df[‘dst’]cudf.Series

Contains the vertex identifiers of the destination of each edge

df[‘edge_betweenness_centrality’]cudf.Series

Contains the betweenness centrality of edges

When using undirected graphs, ‘src’ and ‘dst’ only contains elements such that ‘src’ < ‘dst’, which might differ from networkx and user’s input. Namely edge (1 -> 0) is transformed into (0 -> 1) but contains the betweenness centrality of edge (1 -> 0).

Examples

>>> gdf = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>>                   dtype=['int32', 'int32', 'float32'], header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(gdf, source='0', destination='1')
>>> ebc = cugraph.edge_betweenness_centrality(G)

Edge Betweenness Centrality

Compute the edge betweenness centrality for all edges of the graph G. Betweenness centrality is a measure of the number of shortest paths that pass over an edge. An edge with a high betweenness centrality score has more paths passing over it and is therefore believed to be more important. Rather than doing an all-pair shortest path, a sample of k starting vertices can be used.

CuGraph does not currently support the ‘weight’ parameter as seen in the corresponding networkX call.

Parameters

GcuGraph.Graph or networkx.Graph

The graph can be either directed (DiGraph) or undirected (Graph). Weights in the graph are ignored, the current implementation uses BFS traversals. Use weight parameter if weights need to be considered (currently not supported)

kint or list or None, optional, default=None

If k is not None, use k node samples to estimate betweenness. Higher values give better approximation If k is a list, use the content of the list for estimation: the list should contain vertices identifiers. Vertices obtained through sampling or defined as a list will be used as sources for traversals inside the algorithm.

normalizedbool, optional

Default is True. If true, the betweenness values are normalized by 2 / (n * (n - 1)) for Graphs (undirected), and 1 / (n * (n - 1)) for DiGraphs (directed graphs) where n is the number of nodes in G. Normalization will ensure that values are in [0, 1], this normalization scales for the highest possible value where one edge is crossed by every single shortest path.

weightcudf.DataFrame, optional, default=None

Specifies the weights to be used for each edge. Should contain a mapping between edges and weights. (Not Supported)

seedoptional

if k is specified and k is an integer, use seed to initialize the random number generator. Using None as seed relies on random.seed() behavior: using current system time If k is either None or list: seed parameter is ignored

result_dtypenp.float32 or np.float64, optional, default=np.float64

Indicate the data type of the betweenness centrality scores Using double automatically switch implementation to “default”

Returns

dfcudf.DataFrame or Dictionary if using NetworkX

GPU data frame containing three cudf.Series of size E: the vertex identifiers of the sources, the vertex identifies of the destinations and the corresponding betweenness centrality values. Please note that the resulting the ‘src’, ‘dst’ column might not be in ascending order.

df[‘src’]cudf.Series

Contains the vertex identifiers of the source of each edge

df[‘dst’]cudf.Series

Contains the vertex identifiers of the destination of each edge

df[‘edge_betweenness_centrality’]cudf.Series

Contains the betweenness centrality of edges

When using undirected graphs, ‘src’ and ‘dst’ only contains elements such that ‘src’ < ‘dst’, which might differ from networkx and user’s input. Namely edge (1 -> 0) is transformed into (0 -> 1) but contains the betweenness centrality of edge (1 -> 0).

Examples

>>> gdf = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>>                   dtype=['int32', 'int32', 'float32'], header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(gdf, source='0', destination='1')
>>> ebc = cugraph.edge_betweenness_centrality(G)

Katz Centrality

cugraph.centrality.katz_centrality.katz_centrality(G, alpha=None, beta=None, max_iter=100, tol=1e-06, nstart=None, normalized=True)

Compute the Katz centrality for the nodes of the graph G. cuGraph does not currently support the ‘beta’ and ‘weight’ parameters as seen in the corresponding networkX call. This implementation is based on a relaxed version of Katz defined by Foster with a reduced computational complexity of O(n+m)

Foster, K.C., Muth, S.Q., Potterat, J.J. et al. Computational & Mathematical Organization Theory (2001) 7: 275. https://doi.org/10.1023/A:1013470632383

Parameters

GcuGraph.Graph or networkx.Graph

cuGraph graph descriptor with connectivity information. The graph can contain either directed (DiGraph) or undirected edges (Graph).

alphafloat

Attenuation factor defaulted to None. If alpha is not specified then it is internally calculated as 1/(degree_max) where degree_max is the maximum out degree. NOTE : The maximum acceptable value of alpha for convergence alpha_max = 1/(lambda_max) where lambda_max is the largest eigenvalue of the graph. Since lambda_max is always lesser than or equal to degree_max for a graph, alpha_max will always be greater than or equal to (1/degree_max). Therefore, setting alpha to (1/degree_max) will guarantee that it will never exceed alpha_max thus in turn fulfilling the requirement for convergence.

betaNone

A weight scalar - currently Not Supported

max_iterint

The maximum number of iterations before an answer is returned. This can be used to limit the execution time and do an early exit before the solver reaches the convergence tolerance. If this value is lower or equal to 0 cuGraph will use the default value, which is 100.

tolerancefloat

Set the tolerance the approximation, this parameter should be a small magnitude value. The lower the tolerance the better the approximation. If this value is 0.0f, cuGraph will use the default value which is 1.0e-6. Setting too small a tolerance can lead to non-convergence due to numerical roundoff. Usually values between 1e-2 and 1e-6 are acceptable.

nstartcudf.Dataframe

GPU Dataframe containing the initial guess for katz centrality.

nstart[‘vertex’]cudf.Series

Contains the vertex identifiers

nstart[‘values’]cudf.Series

Contains the katz centrality values of vertices

normalizedbool

If True normalize the resulting katz centrality values

Returns

dfcudf.DataFrame or Dictionary if using NetworkX

GPU data frame containing two cudf.Series of size V: the vertex identifiers and the corresponding katz centrality values.

df[‘vertex’]cudf.Series

Contains the vertex identifiers

df[‘katz_centrality’]cudf.Series

Contains the katz centrality of vertices

Examples

>>> gdf = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>>                   dtype=['int32', 'int32', 'float32'], header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(gdf, source='0', destination='1')
>>> kc = cugraph.katz_centrality(G)

Community

Ensemble clustering for graphs (ECG)

cugraph.community.ecg.ecg(input_graph, min_weight=0.05, ensemble_size=16, weight=None)

Compute the Ensemble Clustering for Graphs (ECG) partition of the input graph. ECG runs truncated Louvain on an ensemble of permutations of the input graph, then uses the ensemble partitions to determine weights for the input graph. The final result is found by running full Louvain on the input graph using the determined weights.

See https://arxiv.org/abs/1809.05578 for further information.

Parameters

input_graphcugraph.Graph or NetworkX Graph

The graph descriptor should contain the connectivity information and weights. The adjacency list will be computed if not already present.

min_weightfloating point

The minimum value to assign as an edgeweight in the ECG algorithm. It should be a value in the range [0,1] usually left as the default value of .05

ensemble_sizeinteger

The number of graph permutations to use for the ensemble. The default value is 16, larger values may produce higher quality partitions for some graphs.

weightstr

This parameter is here for NetworkX compatibility and represents which NetworkX data column represents Edge weights. Default is None

Returns

partscudf.DataFrame or python dictionary

GPU data frame of size V containing two columns, the vertex id and the partition id it is assigned to.

df[vertex]cudf.Series

Contains the vertex identifiers

df[partition]cudf.Series

Contains the partition assigned to the vertices

Examples

>>> M = cudf.read_csv('datasets/karate.csv', delimiter = ' ',
                      dtype=['int32', 'int32', 'float32'],
                      header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(M, source='0', destination='1', edge_attr='2')
>>> parts = cugraph.ecg(G)

K-Truss

cugraph.community.ktruss_subgraph.k_truss(G, k)

Returns the K-Truss subgraph of a graph for a specific k.

The k-truss of a graph is a subgraph where each edge is part of at least (k−2) triangles. K-trusses are used for finding tighlty knit groups of vertices in a graph. A k-truss is a relaxation of a k-clique in the graph and was define in [1]. Finding cliques is computationally demanding and finding the maximal k-clique is known to be NP-Hard.

Parameters

GcuGraph.Graph or networkx.Graph

cuGraph graph descriptor with connectivity information. k-Trusses are defined for only undirected graphs as they are defined for undirected triangle in a graph.

kint

The desired k to be used for extracting the k-truss subgraph.

Returns

G_trusscuGraph.Graph or networkx.Graph

A cugraph graph descriptor with the k-truss subgraph for the given k. The networkx graph will NOT have all attributes copied over

cugraph.community.ktruss_subgraph.ktruss_subgraph(G, k, use_weights=True)

Returns the K-Truss subgraph of a graph for a specific k.

The k-truss of a graph is a subgraph where each edge is part of at least (k−2) triangles. K-trusses are used for finding tighlty knit groups of vertices in a graph. A k-truss is a relaxation of a k-clique in the graph and was define in [1]. Finding cliques is computationally demanding and finding the maximal k-clique is known to be NP-Hard.

In contrast, finding a k-truss is computationally tractable as its key building block, namely triangle counting counting, can be executed in polnymomial time.Typically, it takes many iterations of triangle counting to find the k-truss of a graph. Yet these iterations operate on a weakly monotonically shrinking graph. Therefore, finding the k-truss of a graph can be done in a fairly reasonable amount of time. The solution in cuGraph is based on a GPU algorithm first shown in [2] and uses the triangle counting algorithm from [3].

[1] Cohen, J., “Trusses: Cohesive subgraphs for social network analysis” National security agency technical report, 2008

[2] O. Green, J. Fox, E. Kim, F. Busato, et al. “Quickly Finding a Truss in a Haystack” IEEE High Performance Extreme Computing Conference (HPEC), 2017 https://doi.org/10.1109/HPEC.2017.8091038

[3] O. Green, P. Yalamanchili, L.M. Munguia, “Fast Triangle Counting on GPU” Irregular Applications: Architectures and Algorithms (IA3), 2014

Parameters

GcuGraph.Graph

cuGraph graph descriptor with connectivity information. k-Trusses are defined for only undirected graphs as they are defined for undirected triangle in a graph.

kint

The desired k to be used for extracting the k-truss subgraph.

use_weightsBool

whether the output should contain the edge weights if G has them

Returns

G_trusscuGraph.Graph

A cugraph graph descriptor with the k-truss subgraph for the given k.

Examples

>>> gdf = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>>                   dtype=['int32', 'int32', 'float32'], header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(gdf, source='0', destination='1')
>>> k_subgraph = cugraph.ktruss_subgraph(G, 3)

Leiden

cugraph.community.leiden.leiden(G, max_iter=100, resolution=1.0)

Compute the modularity optimizing partition of the input graph using the Leiden algorithm

It uses the Louvain method described in:

Traag, V. A., Waltman, L., & van Eck, N. J. (2019). From Louvain to Leiden: guaranteeing well-connected communities. Scientific reports, 9(1), 5233. doi: 10.1038/s41598-019-41695-z

Parameters

Gcugraph.Graph

cuGraph graph descriptor of type Graph

The adjacency list will be computed if not already present.

max_iterinteger

This controls the maximum number of levels/iterations of the Leiden algorithm. When specified the algorithm will terminate after no more than the specified number of iterations. No error occurs when the algorithm terminates early in this manner.

resolution: float/double, optional

Called gamma in the modularity formula, this changes the size of the communities. Higher resolutions lead to more smaller communities, lower resolutions lead to fewer larger communities. Defaults to 1.

Returns

partscudf.DataFrame

GPU data frame of size V containing two columns the vertex id and the partition id it is assigned to.

df[‘vertex’]cudf.Series

Contains the vertex identifiers

df[‘partition’]cudf.Series

Contains the partition assigned to the vertices

modularity_scorefloat

a floating point number containing the global modularity score of the partitioning.

Examples

>>> M = cudf.read_csv('datasets/karate.csv',
                      delimiter = ' ',
                      dtype=['int32', 'int32', 'float32'],
                      header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(M, source='0', destination='1')
>>> parts, modularity_score = cugraph.leiden(G)

Louvain

cugraph.community.louvain.louvain(G, max_iter=100, resolution=1.0)

Compute the modularity optimizing partition of the input graph using the Louvain method

It uses the Louvain method described in:

VD Blondel, J-L Guillaume, R Lambiotte and E Lefebvre: Fast unfolding of community hierarchies in large networks, J Stat Mech P10008 (2008), http://arxiv.org/abs/0803.0476

Parameters

Gcugraph.Graph or NetworkX Graph

The graph descriptor should contain the connectivity information and weights. The adjacency list will be computed if not already present.

max_iterinteger

This controls the maximum number of levels/iterations of the Louvain algorithm. When specified the algorithm will terminate after no more than the specified number of iterations. No error occurs when the algorithm terminates early in this manner.

resolution: float/double, optional

Called gamma in the modularity formula, this changes the size of the communities. Higher resolutions lead to more smaller communities, lower resolutions lead to fewer larger communities. Defaults to 1.

Returns

partscudf.DataFrame

GPU data frame of size V containing two columns the vertex id and the partition id it is assigned to.

df[‘vertex’]cudf.Series

Contains the vertex identifiers

df[‘partition’]cudf.Series

Contains the partition assigned to the vertices

modularity_scorefloat

a floating point number containing the global modularity score of the partitioning.

Examples

>>> M = cudf.read_csv('datasets/karate.csv',
                      delimiter = ' ',
                      dtype=['int32', 'int32', 'float32'],
                      header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(M, source='0', destination='1')
>>> parts, modularity_score = cugraph.louvain(G)

Spectral Clustering

cugraph.community.spectral_clustering.analyzeClustering_edge_cut(G, n_clusters, clustering, vertex_col_name='vertex', cluster_col_name='cluster')

Compute the edge cut score for a partitioning/clustering

Parameters

Gcugraph.Graph

cuGraph graph descriptor

n_clustersinteger

Specifies the number of clusters in the given clustering

clusteringcudf.DataFrame

The cluster assignment to analyze.

vertex_col_namestr

The name of the column in the clustering dataframe identifying the external vertex id

cluster_col_namestr

The name of the column in the clustering dataframe identifying the cluster id

Returns

scorefloat

The computed edge cut score

Examples

>>> M = cudf.read_csv('datasets/karate.csv',
                      delimiter = ' ',
                      dtype=['int32', 'int32', 'float32'],
                      header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(M, source='0', destination='1', edge_attr=None)
>>> df = cugraph.spectralBalancedCutClustering(G, 5)
>>> score = cugraph.analyzeClustering_edge_cut(G, 5, df,
>>>   'vertex', 'cluster')

cugraph.community.spectral_clustering.analyzeClustering_modularity(G, n_clusters, clustering, vertex_col_name='vertex', cluster_col_name='cluster')

Compute the modularity score for a partitioning/clustering

Parameters

Gcugraph.Graph

cuGraph graph descriptor. This graph should have edge weights.

n_clustersinteger

Specifies the number of clusters in the given clustering

clusteringcudf.DataFrame

The cluster assignment to analyze.

vertex_col_namestr

The name of the column in the clustering dataframe identifying the external vertex id

cluster_col_namestr

The name of the column in the clustering dataframe identifying the cluster id

Returns

scorefloat

The computed modularity score

Examples

>>> M = cudf.read_csv('datasets/karate.csv',
                      delimiter = ' ',
                      dtype=['int32', 'int32', 'float32'],
                      header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(M, source='0', destination='1', edge_attr='2')
>>> df = cugraph.spectralBalancedCutClustering(G, 5)
>>> score = cugraph.analyzeClustering_modularity(G, 5, df,
>>>   'vertex', 'cluster')

cugraph.community.spectral_clustering.analyzeClustering_ratio_cut(G, n_clusters, clustering, vertex_col_name='vertex', cluster_col_name='cluster')

Compute the ratio cut score for a partitioning/clustering

Parameters

Gcugraph.Graph

cuGraph graph descriptor. This graph should have edge weights.

n_clustersinteger

Specifies the number of clusters in the given clustering

clusteringcudf.DataFrame

The cluster assignment to analyze.

vertex_col_namestr

The name of the column in the clustering dataframe identifying the external vertex id

cluster_col_namestr

The name of the column in the clustering dataframe identifying the cluster id

Returns

scorefloat

The computed ratio cut score

Examples

>>> M = cudf.read_csv('datasets/karate.csv',
                      delimiter = ' ',
                      dtype=['int32', 'int32', 'float32'],
                      header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(M, source='0', destination='1', edge_attr='2')
>>> df = cugraph.spectralBalancedCutClustering(G, 5)
>>> score = cugraph.analyzeClustering_ratio_cut(G, 5, df,
>>>   'vertex', 'cluster')

cugraph.community.spectral_clustering.spectralBalancedCutClustering(G, num_clusters, num_eigen_vects=2, evs_tolerance=1e-05, evs_max_iter=100, kmean_tolerance=1e-05, kmean_max_iter=100)

Compute a clustering/partitioning of the given graph using the spectral balanced cut method.

Parameters

Gcugraph.Graph or networkx.Graph

cuGraph graph descriptor

num_clustersinteger

Specifies the number of clusters to find

num_eigen_vectsinteger

Specifies the number of eigenvectors to use. Must be lower or equal to num_clusters.

evs_tolerance: float

Specifies the tolerance to use in the eigensolver Default is 0.00001

evs_max_iter: integer

Specifies the maximum number of iterations for the eigensolver Default is 100

kmean_tolerance: float

Specifies the tolerance to use in the k-means solver Default is 0.00001

kmean_max_iter: integer

Specifies the maximum number of iterations for the k-means solver Default is 100

Returns

dfcudf.DataFrame

GPU data frame containing two cudf.Series of size V: the vertex identifiers and the corresponding cluster assignments.

df[‘vertex’]cudf.Series

contains the vertex identifiers

df[‘cluster’]cudf.Series

contains the cluster assignments

Examples

>>> M = cudf.read_csv('datasets/karate.csv',
                      delimiter = ' ',
                      dtype=['int32', 'int32', 'float32'],
                      header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(M, source='0', destination='1')
>>> df = cugraph.spectralBalancedCutClustering(G, 5)

cugraph.community.spectral_clustering.spectralModularityMaximizationClustering(G, num_clusters, num_eigen_vects=2, evs_tolerance=1e-05, evs_max_iter=100, kmean_tolerance=1e-05, kmean_max_iter=100)

Compute a clustering/partitioning of the given graph using the spectral modularity maximization method.

Parameters

Gcugraph.Graph

cuGraph graph descriptor. This graph should have edge weights.

num_clustersinteger

Specifies the number of clusters to find

num_eigen_vectsinteger

Specifies the number of eigenvectors to use. Must be lower or equal to num_clusters

evs_tolerance: float

Specifies the tolerance to use in the eigensolver Default is 0.00001

evs_max_iter: integer

Specifies the maximum number of iterations for the eigensolver Default is 100

kmean_tolerance: float

Specifies the tolerance to use in the k-means solver Default is 0.00001

kmean_max_iter: integer

Specifies the maximum number of iterations for the k-means solver Default is 100

Returns

dfcudf.DataFrame

df[‘vertex’]cudf.Series

contains the vertex identifiers

df[‘cluster’]cudf.Series

contains the cluster assignments

Examples

>>> M = cudf.read_csv('datasets/karate.csv',
                      delimiter = ' ',
                      dtype=['int32', 'int32', 'float32'],
                      header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(M, source='0', destination='1', edge_attr='2')
>>> df = cugraph.spectralModularityMaximizationClustering(G, 5)

Subgraph Extraction

cugraph.community.subgraph_extraction.subgraph(G, vertices)

Compute a subgraph of the existing graph including only the specified vertices. This algorithm works for both directed and undirected graphs, it does not actually traverse the edges, simply pulls out any edges that are incident on vertices that are both contained in the vertices list.

Parameters

Gcugraph.Graph

cuGraph graph descriptor

verticescudf.Series

Specifies the vertices of the induced subgraph

Returns

Sgcugraph.Graph

A graph object containing the subgraph induced by the given vertex set.

Examples

>>> gdf = cudf.read_csv('datasets/karate.csv',
                      delimiter = ' ',
                      dtype=['int32', 'int32', 'float32'],
                      header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(gdf, source='0', destination='1')
>>> verts = numpy.zeros(3, dtype=numpy.int32)
>>> verts[0] = 0
>>> verts[1] = 1
>>> verts[2] = 2
>>> sverts = cudf.Series(verts)
>>> Sg = cugraph.subgraph(G, sverts)

Triangle Counting

cugraph.community.triangle_count.triangles(G)

Compute the number of triangles (cycles of length three) in the input graph.

Unlike NetworkX, this algorithm simply returns the total number of triangle and not the number per vertex.

Parameters

Gcugraph.graph or networkx.Graph

cuGraph graph descriptor, should contain the connectivity information, (edge weights are not used in this algorithm)

Returns

countint64

A 64 bit integer whose value gives the number of triangles in the graph.

Examples

>>> gdf = cudf.read_csv('datasets/karate.csv',
                      delimiter = ' ',
                      dtype=['int32', 'int32', 'float32'],
                      header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(gdf, source='0', destination='1')
>>> count = cugraph.triangles(G)

Components

Connected Components

cugraph.components.connectivity.connected_components(G, directed=None, connection='weak', return_labels=None)

Generate either the stronlgly or weakly connected components and attach a component label to each vertex.

Parameters

Gcugraph.Graph, networkx.Graph, CuPy or SciPy sparse matrix

Graph or matrix object, which should contain the connectivity information (edge weights are not used for this algorithm). If using a graph object, the graph can be either directed or undirected where an undirected edge is represented by a directed edge in both directions. The adjacency list will be computed if not already present. The number of vertices should fit into a 32b int.

directedbool, optional

NOTE: For non-Graph-type (eg. sparse matrix) values of G only. Raises

TypeError if used with a Graph object.

If True (default), then convert the input matrix to a cugraph.DiGraph and only move from point i to point j along paths csgraph[i, j]. If False, then find the shortest path on an undirected graph: the algorithm can progress from point i to j along csgraph[i, j] or csgraph[j, i].

connectionstr, optional

[‘weak’|’strong’]. Return either weakly or strongly connected components.

return_labelsbool, optional

NOTE: For non-Graph-type (eg. sparse matrix) values of G only. Raises

TypeError if used with a Graph object.

If True (default), then return the labels for each of the connected components.

Returns

Return value type is based on the input type. If G is a cugraph.Graph,

returns:

cudf.DataFrame

GPU data frame containing two cudf.Series of size V: the vertex identifiers and the corresponding component identifier.

df[‘vertex’]

Contains the vertex identifier

df[‘labels’]

The component identifier

If G is a networkx.Graph, returns:

python dictionary, where keys are vertices and values are the component identifiers.

If G is a CuPy or SciPy matrix, returns:

CuPy ndarray (if CuPy matrix input) or Numpy ndarray (if SciPy matrix input) of shape (<num vertices>, 2), where column 0 contains component identifiers and column 1 contains vertices.

Examples

>>> M = cudf.read_csv('datasets/karate.csv',
                      delimiter = ' ',
                      dtype=['int32', 'int32', 'float32'],
                      header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(M, source='0', destination='1', edge_attr=None)
>>> df = cugraph.strongly_connected_components(G)

cugraph.components.connectivity.strongly_connected_components(G, directed=None, connection=None, return_labels=None)

Generate the Strongly Connected Components and attach a component label to each vertex.

Parameters

Gcugraph.Graph, networkx.Graph, CuPy or SciPy sparse matrix

Graph or matrix object, which should contain the connectivity information (edge weights are not used for this algorithm). If using a graph object, the graph can be either directed or undirected where an undirected edge is represented by a directed edge in both directions. The adjacency list will be computed if not already present. The number of vertices should fit into a 32b int.

directedbool, optional

NOTE: For non-Graph-type (eg. sparse matrix) values of G only. Raises

TypeError if used with a Graph object.

If True (default), then convert the input matrix to a cugraph.DiGraph and only move from point i to point j along paths csgraph[i, j]. If False, then find the shortest path on an undirected graph: the algorithm can progress from point i to j along csgraph[i, j] or csgraph[j, i].

connectionstr, optional

Added for SciPy compatibility, can only be specified for non-Graph-type (eg. sparse matrix) values of G only (raises TypeError if used with a Graph object), and can only be set to “strong” for this API.

return_labelsbool, optional

NOTE: For non-Graph-type (eg. sparse matrix) values of G only. Raises

TypeError if used with a Graph object.

If True (default), then return the labels for each of the connected components.

Returns

Return value type is based on the input type. If G is a cugraph.Graph,

returns:

cudf.DataFrame

GPU data frame containing two cudf.Series of size V: the vertex identifiers and the corresponding component identifier.

df[‘vertex’]

Contains the vertex identifier

df[‘labels’]

The component identifier

If G is a networkx.Graph, returns:

python dictionary, where keys are vertices and values are the component identifiers.

If G is a CuPy or SciPy matrix, returns:

CuPy ndarray (if CuPy matrix input) or Numpy ndarray (if SciPy matrix input) of shape (<num vertices>, 2), where column 0 contains component identifiers and column 1 contains vertices.

Examples

>>> M = cudf.read_csv('datasets/karate.csv',
                      delimiter = ' ',
                      dtype=['int32', 'int32', 'float32'],
                      header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(M, source='0', destination='1', edge_attr=None)
>>> df = cugraph.strongly_connected_components(G)

cugraph.components.connectivity.weakly_connected_components(G, directed=None, connection=None, return_labels=None)

Generate the Weakly Connected Components and attach a component label to each vertex.

Parameters

Gcugraph.Graph, networkx.Graph, CuPy or SciPy sparse matrix

Graph or matrix object, which should contain the connectivity information (edge weights are not used for this algorithm). If using a graph object, the graph can be either directed or undirected where an undirected edge is represented by a directed edge in both directions. The adjacency list will be computed if not already present. The number of vertices should fit into a 32b int.

directedbool, optional

NOTE: For non-Graph-type (eg. sparse matrix) values of G only. Raises

TypeError if used with a Graph object.

If True (default), then convert the input matrix to a cugraph.DiGraph and only move from point i to point j along paths csgraph[i, j]. If False, then find the shortest path on an undirected graph: the algorithm can progress from point i to j along csgraph[i, j] or csgraph[j, i].

connectionstr, optional

Added for SciPy compatibility, can only be specified for non-Graph-type (eg. sparse matrix) values of G only (raises TypeError if used with a Graph object), and can only be set to “weak” for this API.

return_labelsbool, optional

NOTE: For non-Graph-type (eg. sparse matrix) values of G only. Raises

TypeError if used with a Graph object.

If True (default), then return the labels for each of the connected components.

Returns

Return value type is based on the input type. If G is a cugraph.Graph,

returns:

cudf.DataFrame

GPU data frame containing two cudf.Series of size V: the vertex identifiers and the corresponding component identifier.

df[‘vertex’]

Contains the vertex identifier

df[‘labels’]

The component identifier

If G is a networkx.Graph, returns:

python dictionary, where keys are vertices and values are the component identifiers.

If G is a CuPy or SciPy matrix, returns:

CuPy ndarray (if CuPy matrix input) or Numpy ndarray (if SciPy matrix input) of shape (<num vertices>, 2), where column 0 contains component identifiers and column 1 contains vertices.

Examples

>>> M = cudf.read_csv('datasets/karate.csv',
                      delimiter = ' ',
                      dtype=['int32', 'int32', 'float32'],
                      header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(M, source='0', destination='1', edge_attr=None)
>>> df = cugraph.weakly_connected_components(G)

Cores

Core Number

cugraph.cores.core_number.core_number(G)

Compute the core numbers for the nodes of the graph G. A k-core of a graph is a maximal subgraph that contains nodes of degree k or more. A node has a core number of k if it belongs a k-core but not to k+1-core. This call does not support a graph with self-loops and parallel edges.

Parameters

graphcuGraph.Graph or networkx.Graph

The graph should contain undirected edges where undirected edges are represented as directed edges in both directions. While this graph can contain edge weights, they don’t participate in the calculation of the core numbers.

Returns

dfcudf.DataFrame or python dictionary (in NetworkX input)

GPU data frame containing two cudf.Series of size V: the vertex identifiers and the corresponding core number values.

df[‘vertex’]cudf.Series

Contains the vertex identifiers

df[‘core_number’]cudf.Series

Contains the core number of vertices

Examples

>>> gdf = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>>                   dtype=['int32', 'int32', 'float32'], header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(gdf, source='0', destination='1')
>>> cn = cugraph.core_number(G)

K-Core

cugraph.cores.k_core.k_core(G, k=None, core_number=None)

Compute the k-core of the graph G based on the out degree of its nodes. A k-core of a graph is a maximal subgraph that contains nodes of degree k or more. This call does not support a graph with self-loops and parallel edges.

Parameters

GcuGraph.Graph or networkx.Graph

cuGraph graph descriptor with connectivity information. The graph should contain undirected edges where undirected edges are represented as directed edges in both directions. While this graph can contain edge weights, they don’t participate in the calculation of the k-core.

kint, optional

Order of the core. This value must not be negative. If set to None, the main core is returned.

core_numbercudf.DataFrame, optional

Precomputed core number of the nodes of the graph G containing two cudf.Series of size V: the vertex identifiers and the corresponding core number values. If set to None, the core numbers of the nodes are calculated internally.

core_number[‘vertex’]cudf.Series

Contains the vertex identifiers

core_number[‘values’]cudf.Series

Contains the core number of vertices

Returns

KCoreGraphcuGraph.Graph

K Core of the input graph

Examples

>>> gdf = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>>                   dtype=['int32', 'int32', 'float32'], header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(gdf, source='0', destination='1')
>>> KCoreGraph = cugraph.k_core(G)

Layout

Force Atlas 2

cugraph.layout.force_atlas2.force_atlas2(input_graph, max_iter=500, pos_list=None, outbound_attraction_distribution=True, lin_log_mode=False, prevent_overlapping=False, edge_weight_influence=1.0, jitter_tolerance=1.0, barnes_hut_optimize=True, barnes_hut_theta=0.5, scaling_ratio=2.0, strong_gravity_mode=False, gravity=1.0, verbose=False, callback=None)

ForceAtlas2 is a continuous graph layout algorithm for handy network visualization.

NOTE: Peak memory allocation occurs at 30*V.

Parameters

input_graphcugraph.Graph

cuGraph graph descriptor with connectivity information. Edge weights, if present, should be single or double precision floating point values.

max_iterinteger

This controls the maximum number of levels/iterations of the Force Atlas algorithm. When specified the algorithm will terminate after no more than the specified number of iterations. No error occurs when the algorithm terminates in this manner. Good short-term quality can be achieved with 50-100 iterations. Above 1000 iterations is discouraged.

pos_list: cudf.DataFrame

Data frame with initial vertex positions containing two columns: ‘x’ and ‘y’ positions.

outbound_attraction_distribution: bool

Distributes attraction along outbound edges. Hubs attract less and thus are pushed to the borders.

lin_log_mode: bool

Switch Force Atlas model from lin-lin to lin-log. Makes clusters more tight.

prevent_overlapping: bool

Prevent nodes to overlap.

edge_weight_influence: float

How much influence you give to the edges weight. 0 is “no influence” and 1 is “normal”.

jitter_tolerance: float

How much swinging you allow. Above 1 discouraged. Lower gives less speed and more precision.

barnes_hut_optimize: bool

Whether to use the Barnes Hut approximation or the slower exact version.

barnes_hut_theta: float

Float between 0 and 1. Tradeoff for speed (1) vs accuracy (0) for Barnes Hut only.

scaling_ratio: float

How much repulsion you want. More makes a more sparse graph. Switching from regular mode to LinLog mode needs a readjustment of the scaling parameter.

strong_gravity_mode: bool

Sets a force that attracts the nodes that are distant from the center more. It is so strong that it can sometimes dominate other forces.

gravityfloat

Attracts nodes to the center. Prevents islands from drifting away.

verbose: bool

Output convergence info at each interation.

callback: GraphBasedDimRedCallback

An instance of GraphBasedDimRedCallback class to intercept the internal state of positions while they are being trained.

Example of callback usage:

from cugraph.internals import GraphBasedDimRedCallback

class CustomCallback(GraphBasedDimRedCallback):

def on_preprocess_end(self, positions):

print(positions.copy_to_host())

def on_epoch_end(self, positions):

print(positions.copy_to_host())

def on_train_end(self, positions):

print(positions.copy_to_host())

Returns

poscudf.DataFrame

GPU data frame of size V containing three columns: the vertex identifiers and the x and y positions.

Link Analysis

HITS

cugraph.link_analysis.hits.hits(G, max_iter=100, tol=1e-05, nstart=None, normalized=True)

Compute HITS hubs and authorities values for each vertex

The HITS algorithm computes two numbers for a node. Authorities estimates the node value based on the incoming links. Hubs estimates the node value based on outgoing links.

The cuGraph implementation of HITS is a wrapper around the gunrock implementation of HITS.

Note that the gunrock implementation uses a 2-norm, while networkx uses a 1-norm. The raw scores will be different, but the rank ordering should be comparable with networkx.

Parameters

graphcugraph.Graph

cuGraph graph descriptor, should contain the connectivity information as an edge list (edge weights are not used for this algorithm). The adjacency list will be computed if not already present.

max_iterint

The maximum number of iterations before an answer is returned. The gunrock implementation does not currently support tolerance, so this will in fact be the number of iterations the HITS algorithm executes.

tolerancefloat

Set the tolerance the approximation, this parameter should be a small magnitude value. This parameter is not currently supported.

nstartcudf.Dataframe

Not currently supported

normalizedbool

Not currently supported, always used as True

Returns

HubsAndAuthoritiescudf.DataFrame

GPU data frame containing three cudf.Series of size V: the vertex identifiers and the corresponding hubs values and the corresponding authorities values.

df[‘vertex’]cudf.Series

Contains the vertex identifiers

df[‘hubs’]cudf.Series

Contains the hubs score

df[‘authorities’]cudf.Series

Contains the authorities score

Examples

>>> gdf = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>>                   dtype=['int32', 'int32', 'float32'], header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(gdf, source='0', destination='1')
>>> hits = cugraph.hits(G, max_iter = 50)

Pagerank

cugraph.link_analysis.pagerank.pagerank(G, alpha=0.85, personalization=None, max_iter=100, tol=1e-05, nstart=None, weight=None, dangling=None)

Find the PageRank score for every vertex in a graph. cuGraph computes an approximation of the Pagerank eigenvector using the power method. The number of iterations depends on the properties of the network itself; it increases when the tolerance descreases and/or alpha increases toward the limiting value of 1. The user is free to use default values or to provide inputs for the initial guess, tolerance and maximum number of iterations.

Parameters

graphcugraph.Graph or networkx.Graph

cuGraph graph descriptor, should contain the connectivity information as an edge list (edge weights are not used for this algorithm). The transposed adjacency list will be computed if not already present.

alphafloat

The damping factor alpha represents the probability to follow an outgoing edge, standard value is 0.85. Thus, 1.0-alpha is the probability to “teleport” to a random vertex. Alpha should be greater than 0.0 and strictly lower than 1.0.

personalizationcudf.Dataframe

GPU Dataframe containing the personalization information.

personalization[‘vertex’]cudf.Series

Subset of vertices of graph for personalization

personalization[‘values’]cudf.Series

Personalization values for vertices

max_iterint

The maximum number of iterations before an answer is returned. This can be used to limit the execution time and do an early exit before the solver reaches the convergence tolerance. If this value is lower or equal to 0 cuGraph will use the default value, which is 100.

tolerancefloat

Set the tolerance the approximation, this parameter should be a small magnitude value. The lower the tolerance the better the approximation. If this value is 0.0f, cuGraph will use the default value which is 1.0E-5. Setting too small a tolerance can lead to non-convergence due to numerical roundoff. Usually values between 0.01 and 0.00001 are acceptable.

nstartcudf.Dataframe

GPU Dataframe containing the initial guess for pagerank.

nstart[‘vertex’]cudf.Series

Subset of vertices of graph for initial guess for pagerank values

nstart[‘values’]cudf.Series

Pagerank values for vertices

weightstr

Edge data column to use. Default is None This version of PageRank current does not use edge weight. This parameter is here for NetworkX compatibility

danglingdict

This parameter is here for NetworkX compatibility and ignored

Returns

PageRankcudf.DataFrame

GPU data frame containing two cudf.Series of size V: the vertex identifiers and the corresponding PageRank values.

df[‘vertex’]cudf.Series

Contains the vertex identifiers

df[‘pagerank’]cudf.Series

Contains the PageRank score

Examples

>>> gdf = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>>                   dtype=['int32', 'int32', 'float32'], header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(gdf, source='0', destination='1')
>>> pr = cugraph.pagerank(G, alpha = 0.85, max_iter = 500, tol = 1.0e-05)

Link Prediction

Jaccard Coefficient

cugraph.link_prediction.jaccard.jaccard(input_graph, vertex_pair=None)

Compute the Jaccard similarity between each pair of vertices connected by an edge, or between arbitrary pairs of vertices specified by the user. Jaccard similarity is defined between two sets as the ratio of the volume of their intersection divided by the volume of their union. In the context of graphs, the neighborhood of a vertex is seen as a set. The Jaccard similarity weight of each edge represents the strength of connection between vertices based on the relative similarity of their neighbors. If first is specified but second is not, or vice versa, an exception will be thrown.

NOTE: If the vertex_pair parameter is not specified then the behavior of cugraph.jaccard is different from the behavior of networkx.jaccard_coefficient.

cugraph.jaccard, in the absence of a specified vertex pair list, will use the edges of the graph to construct a vertex pair list and will return the jaccard coefficient for those vertex pairs.

networkx.jaccard_coefficient, in the absence of a specified vertex pair list, will return an upper triangular dense matrix, excluding the diagonal as well as vertex pairs that are directly connected by an edge in the graph, of jaccard coefficients. Technically, networkx returns a lazy iterator across this upper triangular matrix where the actual jaccard coefficient is computed when the iterator is dereferenced. Computing a dense matrix of results is not feasible if the number of vertices in the graph is large (100,000 vertices would result in 4.9 billion values in that iterator).

If your graph is small enough (or you have enough memory and patience) you can get the interesting (non-zero) values that are part of the networkx solution by doing the following:

>>> gdf = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>>                   dtype=['int32', 'int32', 'float32'], header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(gdf, source='0', destination='1')
>>> pairs = cugraph.get_two_hop_neighbors(G)
>>> df = cugraph.jaccard(G, pairs)

But please remember that cugraph will fill the dataframe with the entire solution you request, so you’ll need enough memory to store the 2-hop neighborhood dataframe.

Parameters

graphcugraph.Graph

cuGraph graph descriptor, should contain the connectivity information as an edge list (edge weights are not used for this algorithm). The graph should be undirected where an undirected edge is represented by a directed edge in both direction. The adjacency list will be computed if not already present.

vertex_paircudf.DataFrame

A GPU dataframe consisting of two columns representing pairs of vertices. If provided, the jaccard coefficient is computed for the given vertex pairs. If the vertex_pair is not provided then the current implementation computes the jaccard coefficient for all adjacent vertices in the graph.

Returns

dfcudf.DataFrame

GPU data frame of size E (the default) or the size of the given pairs (first, second) containing the Jaccard weights. The ordering is relative to the adjacency list, or that given by the specified vertex pairs.

df[‘source’]cudf.Series

The source vertex ID (will be identical to first if specified)

df[‘destination’]cudf.Series

The destination vertex ID (will be identical to second if specified)

df[‘jaccard_coeff’]cudf.Series

The computed Jaccard coefficient between the source and destination vertices

Examples

>>> gdf = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>>                   dtype=['int32', 'int32', 'float32'], header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(gdf, source='0', destination='1')
>>> df = cugraph.jaccard(G)

cugraph.link_prediction.jaccard.jaccard_coefficient(G, ebunch=None)

For NetworkX Compatability. See jaccard

Parameters

graphcugraph.Graph

cuGraph graph descriptor, should contain the connectivity information as an edge list (edge weights are not used for this algorithm). The graph should be undirected where an undirected edge is represented by a directed edge in both direction. The adjacency list will be computed if not already present.

ebunchcudf.DataFrame

A GPU dataframe consisting of two columns representing pairs of vertices. If provided, the jaccard coefficient is computed for the given vertex pairs. If the vertex_pair is not provided then the current implementation computes the jaccard coefficient for all adjacent vertices in the graph.

Returns

dfcudf.DataFrame

GPU data frame of size E (the default) or the size of the given pairs (first, second) containing the Jaccard weights. The ordering is relative to the adjacency list, or that given by the specified vertex pairs.

df[‘source’]cudf.Series

The source vertex ID (will be identical to first if specified)

df[‘destination’]cudf.Series

The destination vertex ID (will be identical to second if specified)

df[‘jaccard_coeff’]cudf.Series

The computed Jaccard coefficient between the source and destination vertices

Examples

>>> gdf = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>>                   dtype=['int32', 'int32', 'float32'], header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(gdf, source='0', destination='1')
>>> df = cugraph.jaccard_coefficient(G)

cugraph.link_prediction.wjaccard.jaccard_w(input_graph, weights, vertex_pair=None)

Compute the weighted Jaccard similarity between each pair of vertices connected by an edge, or between arbitrary pairs of vertices specified by the user. Jaccard similarity is defined between two sets as the ratio of the volume of their intersection divided by the volume of their union. In the context of graphs, the neighborhood of a vertex is seen as a set. The Jaccard similarity weight of each edge represents the strength of connection between vertices based on the relative similarity of their neighbors. If first is specified but second is not, or vice versa, an exception will be thrown.

Parameters

graphcugraph.Graph

cuGraph graph descriptor, should contain the connectivity information as an edge list (edge weights are not used for this algorithm). The adjacency list will be computed if not already present.

weightscudf.Series

Specifies the weights to be used for each vertex.

vertex_paircudf.DataFrame

A GPU dataframe consisting of two columns representing pairs of vertices. If provided, the jaccard coefficient is computed for the given vertex pairs, else, it is computed for all vertex pairs.

Returns

dfcudf.DataFrame

GPU data frame of size E (the default) or the size of the given pairs (first, second) containing the Jaccard weights. The ordering is relative to the adjacency list, or that given by the specified vertex pairs.

df[‘source’]cudf.Series

The source vertex ID

df[‘destination’]cudf.Series

The destination vertex ID

df[‘jaccard_coeff’]cudf.Series

The computed weighted Jaccard coefficient between the source and destination vertices.

Examples

>>> M = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>>                   dtype=['int32', 'int32', 'float32'], header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(M, source='0', destination='1')
>>> df = cugraph.jaccard_w(G, M[2])

Overlap Coefficient

cugraph.link_prediction.overlap.overlap(input_graph, vertex_pair=None)

Compute the Overlap Coefficient between each pair of vertices connected by an edge, or between arbitrary pairs of vertices specified by the user. Overlap Coefficient is defined between two sets as the ratio of the volume of their intersection divided by the smaller of their two volumes. In the context of graphs, the neighborhood of a vertex is seen as a set. The Overlap Coefficient weight of each edge represents the strength of connection between vertices based on the relative similarity of their neighbors. If first is specified but second is not, or vice versa, an exception will be thrown.

Parameters

graphcugraph.Graph

cuGraph graph descriptor, should contain the connectivity information as an edge list (edge weights are not used for this algorithm). The adjacency list will be computed if not already present.

vertex_paircudf.DataFrame

A GPU dataframe consisting of two columns representing pairs of vertices. If provided, the overlap coefficient is computed for the given vertex pairs, else, it is computed for all vertex pairs.

Returns

dfcudf.DataFrame

GPU data frame of size E (the default) or the size of the given pairs (first, second) containing the Overlap coefficients. The ordering is relative to the adjacency list, or that given by the specified vertex pairs.

df[‘source’]cudf.Series

The source vertex ID (will be identical to first if specified).

df[‘destination’]cudf.Series

The destination vertex ID (will be identical to second if specified).

df[‘overlap_coeff’]cudf.Series

The computed Overlap coefficient between the source and destination vertices.

Examples

>>> gdf = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>>                   dtype=['int32', 'int32', 'float32'], header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(gdf, source='0', destination='1')
>>> df = cugraph.overlap(G)

cugraph.link_prediction.overlap.overlap_coefficient(G, ebunch=None)

NetworkX similar API. See ‘jaccard’ for a description

cugraph.link_prediction.woverlap.overlap_w(input_graph, weights, vertex_pair=None)

Compute the weighted Overlap Coefficient between each pair of vertices connected by an edge, or between arbitrary pairs of vertices specified by the user. Overlap Coefficient is defined between two sets as the ratio of the volume of their intersection divided by the smaller of their volumes. In the context of graphs, the neighborhood of a vertex is seen as a set. The Overlap Coefficient weight of each edge represents the strength of connection between vertices based on the relative similarity of their neighbors. If first is specified but second is not, or vice versa, an exception will be thrown.

Parameters

input_graphcugraph.Graph

cuGraph graph descriptor, should contain the connectivity information as an edge list (edge weights are not used for this algorithm). The adjacency list will be computed if not already present.

weightscudf.Series

Specifies the weights to be used for each vertex.

vertex_paircudf.DataFrame

A GPU dataframe consisting of two columns representing pairs of vertices. If provided, the overlap coefficient is computed for the given vertex pairs, else, it is computed for all vertex pairs.

Returns

dfcudf.DataFrame

GPU data frame of size E (the default) or the size of the given pairs (first, second) containing the overlap coefficients. The ordering is relative to the adjacency list, or that given by the specified vertex pairs.

df[‘source’]cudf.Series

The source vertex ID

df[‘destination’]cudf.Series

The destination vertex ID

df[‘overlap_coeff’]cudf.Series

The computed weighted Overlap coefficient between the source and destination vertices.

Examples

>>> M = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>>                   dtype=['int32', 'int32', 'float32'], header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(M, source='0', destination='1')
>>> df = cugraph.overlap_w(G, M[2])

Traversal

Breadth-first-search

cugraph.traversal.bfs.bfs(G, start=None, return_sp_counter=None, i_start=None, directed=None, return_predecessors=None)

Find the distances and predecessors for a breadth first traversal of a graph.

Parameters

Gcugraph.Graph, networkx.Graph, CuPy or SciPy sparse matrix

Graph or matrix object, which should contain the connectivity information. Edge weights, if present, should be single or double precision floating point values.

startInteger

The index of the graph vertex from which the traversal begins

return_sp_counterbool, optional, default=False

Indicates if shortest path counters should be returned

i_startInteger, optional

Identical to start, added for API compatibility. Only start or i_start can be set, not both.

directedbool, optional

NOTE: For non-Graph-type (eg. sparse matrix) values of G only. Raises

TypeError if used with a Graph object.

If True (default), then convert the input matrix to a cugraph.DiGraph, otherwise a cugraph.Graph object will be used.

Returns

Return value type is based on the input type. If G is a cugraph.Graph,

returns:

cudf.DataFrame

df[‘vertex’] vertex IDs

df[‘distance’] path distance for each vertex from the starting vertex

df[‘predecessor’] for each i’th position in the column, the vertex ID immediately preceding the vertex at position i in the ‘vertex’ column

df[‘sp_counter’] for each i’th position in the column, the number of shortest paths leading to the vertex at position i in the ‘vertex’ column (Only if retrun_sp_counter is True)

If G is a networkx.Graph, returns:

pandas.DataFrame with contents equivalent to the cudf.DataFrame described above.

If G is a CuPy or SciPy matrix, returns:

a 2-tuple of CuPy ndarrays (if CuPy matrix input) or Numpy ndarrays (if SciPy matrix input) representing:

distance: cupy or numpy ndarray

ndarray of shortest distances between source and vertex.

predecessor: cupy or numpy ndarray

ndarray of predecessors of a vertex on the path from source, which can be used to reconstruct the shortest paths.

…or if return_sp_counter is True, returns a 3-tuple with the above two arrays plus:

sp_counter: cupy or numpy ndarray

ndarray of number of shortest paths leading to each vertex.

Examples

>>> M = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>>                   dtype=['int32', 'int32', 'float32'], header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(M, source='0', destination='1')
>>> df = cugraph.bfs(G, 0)

cugraph.traversal.bfs.bfs_edges(G, source, reverse=False, depth_limit=None, sort_neighbors=None, return_sp_counter=False)

Find the distances and predecessors for a breadth first traversal of a graph.

Parameters

Gcugraph.Graph, networkx.Graph, CuPy or SciPy sparse matrix

Graph or matrix object, which should contain the connectivity information. Edge weights, if present, should be single or double precision floating point values.

sourceInteger

The starting vertex index

reverseboolean

If a directed graph, then process edges in a reverse direction Currently not implemented

depth_limitInt or None

Limit the depth of the search Currently not implemented

sort_neighborsNone or Function

Currently not implemented

return_sp_counterbool, optional, default=False

Indicates if shortest path counters should be returned

Returns

Return value type is based on the input type. If G is a cugraph.Graph,

returns:

cudf.DataFrame

df[‘vertex’] vertex IDs

df[‘distance’] path distance for each vertex from the starting vertex

df[‘predecessor’] for each i’th position in the column, the vertex ID immediately preceding the vertex at position i in the ‘vertex’ column

df[‘sp_counter’] for each i’th position in the column, the number of shortest paths leading to the vertex at position i in the ‘vertex’ column (Only if retrun_sp_counter is True)

If G is a networkx.Graph, returns:

pandas.DataFrame with contents equivalent to the cudf.DataFrame described above.

If G is a CuPy or SciPy matrix, returns:

a 2-tuple of CuPy ndarrays (if CuPy matrix input) or Numpy ndarrays (if SciPy matrix input) representing:

distance: cupy or numpy ndarray

ndarray of shortest distances between source and vertex.

predecessor: cupy or numpy ndarray

ndarray of predecessors of a vertex on the path from source, which can be used to reconstruct the shortest paths.

…or if return_sp_counter is True, returns a 3-tuple with the above two arrays plus:

sp_counter: cupy or numpy ndarray

ndarray of number of shortest paths leading to each vertex.

Examples

>>> M = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>>                   dtype=['int32', 'int32', 'float32'], header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(M, source='0', destination='1')
>>> df = cugraph.bfs_edges(G, 0)

Single-source-shortest-path

cugraph.traversal.sssp.filter_unreachable(df)

Remove unreachable vertices from the result of SSSP or BFS

Parameters

dfcudf.DataFrame

cudf.DataFrame that is the output of SSSP or BFS

Returns

dffiltered cudf.DataFrame with only reachable vertices

df[‘vertex’][i] gives the vertex id of the i’th vertex. df[‘distance’][i] gives the path distance for the i’th vertex from the starting vertex. df[‘predecessor’][i] gives the vertex that was reached before the i’th vertex in the traversal.

cugraph.traversal.sssp.shortest_path(G, source=None, method=None, directed=None, return_predecessors=None, unweighted=None, overwrite=None, indices=None)

Alias for sssp(), provided for API compatibility with NetworkX. See sssp() for details.

cugraph.traversal.sssp.shortest_path_length(G, source, target=None)

Compute the distance from a source vertex to one or all vertexes in graph. Uses Single Source Shortest Path (SSSP).

Parameters

graphcuGraph.Graph, NetworkX.Graph, or CuPy sparse COO matrix

cuGraph graph descriptor with connectivity information. Edge weights, if present, should be single or double precision floating point values.

sourceDependant on graph type. Index of the source vertex.

If graph is an instance of cuGraph.Graph or CuPy sparse COO matrix:

int

If graph is an instance of a NetworkX.Graph:

str

target: Dependant on graph type. Vertex to find distance to.

If graph is an instance of cuGraph.Graph or CuPy sparse COO matrix:

int

If graph is an instance of a NetworkX.Graph:

str

Returns

Return value type is based on the input type.

If target is None, returns:

cudf.DataFrame

df[‘vertex’]

vertex id

df[‘distance’]

gives the path distance from the starting vertex

If target is not None, returns:

Distance from source to target vertex.

cugraph.traversal.sssp.sssp(G, source=None, method=None, directed=None, return_predecessors=None, unweighted=None, overwrite=None, indices=None)

Compute the distance and predecessors for shortest paths from the specified source to all the vertices in the graph. The distances column will store the distance from the source to each vertex. The predecessors column will store each vertex’s predecessor in the shortest path. Vertices that are unreachable will have a distance of infinity denoted by the maximum value of the data type and the predecessor set as -1. The source vertex’s predecessor is also set to -1. Graphs with negative weight cycles are not supported.

Parameters

graphcugraph.Graph, networkx.Graph, CuPy or SciPy sparse matrix Graph or

matrix object, which should contain the connectivity information. Edge weights, if present, should be single or double precision floating point values.

sourceint

Index of the source vertex.

Returns

Return value type is based on the input type. If G is a cugraph.Graph,

returns:

cudf.DataFrame

df[‘vertex’]

vertex id

df[‘distance’]

gives the path distance from the starting vertex

df[‘predecessor’]

the vertex it was reached from

If G is a networkx.Graph, returns:

pandas.DataFrame with contents equivalent to the cudf.DataFrame described above.

If G is a CuPy or SciPy matrix, returns:

a 2-tuple of CuPy ndarrays (if CuPy matrix input) or Numpy ndarrays (if SciPy matrix input) representing:

distance: cupy or numpy ndarray

ndarray of shortest distances between source and vertex.

predecessor: cupy or numpy ndarray

ndarray of predecessors of a vertex on the path from source, which can be used to reconstruct the shortest paths.

Examples

>>> M = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>>                   dtype=['int32', 'int32', 'float32'], header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(M, source='0', destination='1')
>>> distances = cugraph.sssp(G, 0)

Tree

Minimum Spanning Tree

cugraph.tree.minimum_spanning_tree.maximum_spanning_tree(G, weight=None, algorithm='boruvka', ignore_nan=False)

Returns a maximum spanning tree (MST) or forest (MSF) on an undirected graph

Parameters

GcuGraph.Graph or networkx.Graph

cuGraph graph descriptor with connectivity information.

weightstring

default to the weights in the graph, if the graph edges do not have a weight attribute a default weight of 1 will be used.

algorithmstring

Default to ‘boruvka’. The parallel algorithm to use when finding a maximum spanning tree.

ignore_nanbool

Default to False

Returns

——-

G_mstcuGraph.Graph or networkx.Graph

A graph descriptor with a maximum spanning tree or forest. The networkx graph will not have all attributes copied over

cugraph.tree.minimum_spanning_tree.maximum_spanning_tree_subgraph(G)

cugraph.tree.minimum_spanning_tree.minimum_spanning_tree(G, weight=None, algorithm='boruvka', ignore_nan=False)

Returns a minimum spanning tree (MST) or forest (MSF) on an undirected graph

Parameters

GcuGraph.Graph or networkx.Graph

cuGraph graph descriptor with connectivity information.

weightstring

default to the weights in the graph, if the graph edges do not have a weight attribute a default weight of 1 will be used.

algorithmstring

Default to ‘boruvka’. The parallel algorithm to use when finding a minimum spanning tree.

ignore_nanbool

Default to False

Returns

——-

G_mstcuGraph.Graph or networkx.Graph

A graph descriptor with a minimum spanning tree or forest. The networkx graph will not have all attributes copied over

cugraph.tree.minimum_spanning_tree.minimum_spanning_tree_subgraph(G)

Maximum Spanning Tree

Returns a maximum spanning tree (MST) or forest (MSF) on an undirected graph

Parameters

GcuGraph.Graph or networkx.Graph

cuGraph graph descriptor with connectivity information.

weightstring

default to the weights in the graph, if the graph edges do not have a weight attribute a default weight of 1 will be used.

algorithmstring

Default to ‘boruvka’. The parallel algorithm to use when finding a maximum spanning tree.

ignore_nanbool

Default to False

Returns

G_mstcuGraph.Graph or networkx.Graph

A graph descriptor with a maximum spanning tree or forest. The networkx graph will not have all attributes copied over