pylibcugraph.k_truss_subgraph#

pylibcugraph.k_truss_subgraph( ResourceHandle resource_handle, _GPUGraph graph, size_t k, bool_t do_expensive_check, )[source]#

Extract k truss of a graph for a specific k.

Parameters:

resource_handleResourceHandle: Handle to the underlying device resources needed for referencing data and running algorithms.
graphSGGraph or MGGraph: The input graph.
k: size_t: The desired k to be used for extracting the k-truss subgraph.
do_expensive_checkbool_t: If True, performs more extensive tests on the inputs to ensure validitity, at the expense of increased run time.

Returns:

A tuple of device arrays containing the sources, destinations,
edge_weights and edge_offsets.

Examples

>>> import pylibcugraph, cupy, numpy
>>> srcs = cupy.asarray([0, 1, 1, 3, 1, 4, 2, 0, 2, 1, 2,
...     3, 3, 4, 3, 5, 4, 5], dtype=numpy.int32)
>>> dsts = cupy.asarray([1, 0, 3, 1, 4, 1, 0, 2, 1, 2, 3,
...     2, 4, 3, 5, 3, 5, 4], dtype=numpy.int32)
>>> weights = cupy.asarray(
...     [0.1, 0.1, 2.1, 2.1, 1.1, 1.1, 7.2, 7.2, 2.1, 2.1,
...     1.1, 1.1, 7.2, 7.2, 3.2, 3.2, 6.1, 6.1]
...     ,dtype=numpy.float32)
>>> k = 2
>>> resource_handle = pylibcugraph.ResourceHandle()
>>> graph_props = pylibcugraph.GraphProperties(
...     is_symmetric=True, is_multigraph=False)
>>> G = pylibcugraph.SGGraph(
...     resource_handle, graph_props, srcs, dsts, weight_array=weights,
...     store_transposed=False, renumber=False, do_expensive_check=False)
>>> (sources, destinations, edge_weights, subgraph_offsets) =
...     pylibcugraph.k_truss_subgraph(resource_handle, G, k, False)
>>> sources
[0 0 1 1 1 1 2 2 2 3 3 3 3 4 4 4 5 5]
>>> destinations
[1 2 0 2 3 4 0 1 3 1 2 4 5 1 3 5 3 4]
>>> edge_weights
[0.1 7.2 0.1 2.1 2.1 1.1 7.2 2.1 1.1 2.1 1.1 7.2 3.2 1.1 7.2 6.1 3.2 6.1]
>>> subgraph_offsets
[0 18]