cugraph.weakly_connected_components#
- cugraph.weakly_connected_components(G, directed=None, connection=None, return_labels=None)[source]#
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 must be 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 (default=None)
- NOTE
For non-Graph-type (eg. sparse matrix) values of G only. Raises TypeError if used with a Graph object.
If True, then convert the input matrix to a Graph(directed=True) 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 (default=None)
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 (default=True)
- NOTE
For non-Graph-type (eg. sparse matrix) values of G only. Raises TypeError if used with a Graph object.
If True, 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
>>> from cugraph.datasets import karate >>> G = karate.get_graph(download=True) >>> df = cugraph.weakly_connected_components(G)