pylibcugraph.edge_betweenness_centrality

pylibcugraph.edge_betweenness_centrality(

ResourceHandle resource_handle,

_GPUGraph graph,

k,

random_state,

bool_t normalized,

bool_t do_expensive_check,

)

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.

Parameters:

resource_handleResourceHandle

Handle to the underlying device resources needed for referencing data and running algorithms.

graphSGGraph or MGGraph

The input graph, for either Single or Multi-GPU operations.

kint or device array type or None, optional (default=None)

If k is not None, use k node samples to estimate the edge betweenness. Higher values give better approximation. If k is a device array type, the contents are assumed to be vertex identifiers to be used for estimation. If k is None (the default), all the vertices are used to estimate the edge betweenness. Vertices obtained through sampling or defined as a list will be used as sources for traversals inside the algorithm.

random_stateint, optional (default=None)

if k is specified and k is an integer, use random_state to initialize the random number generator. Using None defaults to a hash of process id, time, and hostname If k is either None or list or cudf objects: random_state parameter is ignored.

normalizedbool_t

Normalization will ensure that values are in [0, 1].

do_expensive_checkbool_t

A flag to run expensive checks for input arguments if True.

Returns:

A tuple of device arrays corresponding to the sources, destinations, edge

betweenness centrality scores and edge ids (if provided).

array containing the vertices and the second item in the tuple is a device

array containing the eigenvector centrality scores for the corresponding

vertices.

Examples

>>> import pylibcugraph, cupy, numpy
    ..

>>> srcs = cupy.asarray([0, 1, 1, 2, 2, 2, 3, 4, 1, 3, 4, 0, 1, 3, 5, 5],
    ..

… dtype=numpy.int32)

>>> dsts = cupy.asarray([1, 3, 4, 0, 1, 3, 5, 5, 0, 1, 1, 2, 2, 2, 3, 4],
    ..

… dtype=numpy.int32)

>>> edge_ids = cupy.asarray(
    ..

… [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],

… dtype=numpy.int32)

>>> resource_handle = pylibcugraph.ResourceHandle()
    ..

>>> graph_props = pylibcugraph.GraphProperties(
    ..

… is_symmetric=False, is_multigraph=False)

>>> G = pylibcugraph.SGGraph(
    ..

… resource_handle, graph_props, srcs, dsts, store_transposed=False,

… renumber=False, do_expensive_check=False, edge_id_array=edge_ids)

>>> (srcs, dsts, values, edge_ids) = pylibcugraph.edge_betweenness_centrality(
    resource_handle, G, None, None, True, False)

>>> srcs
    ..

[0 0 1 1 1 1 2 2 2 3 3 3 4 4 5 5]

>>> dsts
    ..

[1 2 0 2 3 4 0 1 3 1 2 5 1 5 3 4]

>>> values
    ..

[0.10555556 0.06111111 0.10555556 0.06666667 0.09444445 0.14444445

0.06111111 0.06666667 0.09444445 0.09444445 0.09444445 0.12222222 0.14444445 0.07777778 0.12222222 0.07777778]

>>> edge_ids
    ..

[ 0 11 8 12 1 2 3 4 5 9 13 6 10 7 14 15]