pylibcugraph.eigenvector_centrality#

pylibcugraph.eigenvector_centrality(ResourceHandle resource_handle, _GPUGraph graph, double epsilon, size_t max_iterations, bool_t do_expensive_check)[source]#

Compute the Eigenvector centrality for the nodes of the graph.

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.
epsilondouble: Error tolerance to check convergence
max_iterations: size_t: Maximum number of Eignevector Centrality iterations
do_expensive_checkbool_t: A flag to run expensive checks for input arguments if True.

Returns:

A tuple of device arrays, where the first item in the tuple is a device
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, 2], dtype=numpy.int32)
>>> dsts = cupy.asarray([1, 2, 3], dtype=numpy.int32)
>>> weights = cupy.asarray([1.0, 1.0, 1.0], dtype=numpy.float32)
>>> resource_handle = pylibcugraph.ResourceHandle()
>>> graph_props = pylibcugraph.GraphProperties(
...     is_symmetric=False, is_multigraph=False)
>>> G = pylibcugraph.SGGraph(
...     resource_handle, graph_props, srcs, dsts, weight_array=weights,
...     store_transposed=True, renumber=False, do_expensive_check=False)
>>> (vertices, values) = pylibcugraph.eigenvector_centrality(
                            resource_handle, G, 1e-6, 1000, False)