Supported Algorithms#

The nx-cugraph backend to NetworkX connects pylibcugraph (cuGraph’s low-level Python interface to its CUDA-based graph analytics library) and CuPy (a GPU-accelerated array library) to NetworkX’s familiar and easy-to-use API.

Below is the list of algorithms that are currently supported in nx-cugraph.

Algorithms#

Centrality

betweenness_centrality

edge_betweenness_centrality

degree_centrality

in_degree_centrality

out_degree_centrality

eigenvector_centrality

katz_centrality

Cluster

average_clustering

clustering

transitivity

triangles

Community

louvain_communities

Bipartite

betweenness_centrality complete_bipartite_graph

Components

connected_components

is_connected

node_connected_component

number_connected_components

weakly_connected

is_weakly_connected

number_weakly_connected_components

weakly_connected_components

Core

core_number

k_truss

DAG

ancestors

descendants

Isolate

is_isolate

isolates

number_of_isolates

Link analysis

hits

pagerank

Operators

complement

reverse

Reciprocity

overall_reciprocity

reciprocity

Shortest Paths

has_path

shortest_path

shortest_path_length

all_pairs_shortest_path

all_pairs_shortest_path_length

bidirectional_shortest_path

single_source_shortest_path

single_source_shortest_path_length

single_target_shortest_path

single_target_shortest_path_length

all_pairs_bellman_ford_path

all_pairs_bellman_ford_path_length

all_pairs_dijkstra

all_pairs_dijkstra_path

all_pairs_dijkstra_path_length

bellman_ford_path

bellman_ford_path_length

dijkstra_path

dijkstra_path_length

single_source_bellman_ford

single_source_bellman_ford_path

single_source_bellman_ford_path_length

single_source_dijkstra

single_source_dijkstra_path

single_source_dijkstra_path_length

Traversal

bfs_edges

bfs_layers

bfs_predecessors

bfs_successors

bfs_tree

descendants_at_distance

generic_bfs_edges

Tree

is_arborescence

is_branching

is_forest

is_tree

Utilities#

Classes

is_negatively_weighted

Convert

from_dict_of_lists

to_dict_of_lists

Convert Matrix

from_pandas_edgelist

from_scipy_sparse_array

Relabel

convert_node_labels_to_integers

relabel_nodes

Generators#

Classic

barbell_graph

circular_ladder_graph

complete_graph

complete_multipartite_graph

cycle_graph

empty_graph

ladder_graph

lollipop_graph

null_graph

path_graph

star_graph

tadpole_graph

trivial_graph

turan_graph

wheel_graph

Classic

caveman_graph

Ego

ego_graph

small

bull_graph

chvatal_graph

cubical_graph

desargues_graph

diamond_graph

dodecahedral_graph

frucht_graph

heawood_graph

house_graph

house_x_graph

icosahedral_graph

krackhardt_kite_graph

moebius_kantor_graph

octahedral_graph

pappus_graph

petersen_graph

sedgewick_maze_graph

tetrahedral_graph

truncated_cube_graph

truncated_tetrahedron_graph

tutte_graph

Social

davis_southern_women_graph

florentine_families_graph

karate_club_graph

les_miserables_graph

To request nx-cugraph backend support for a NetworkX API that is not listed above, visit the nx-cugraph GitHub repo.