pylibcugraph.betweenness_centrality#
- pylibcugraph.betweenness_centrality(
- ResourceHandle resource_handle,
- _GPUGraph graph,
- k,
- random_state,
- bool_t normalized,
- bool_t include_endpoints,
- bool_t do_expensive_check,
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.
- 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 betweenness. Higher values give better approximation. If k is a device array type, use the content of the list for estimation: the list should contain vertex 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.
- 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].
- include_endpointsbool_t
If true, include the endpoints in the shortest path counts.
- do_expensive_checkbool_t
A flag to run expensive checks for input arguments if True.
- Returns: