pylibcugraph.decompress_to_edgelist#

pylibcugraph.decompress_to_edgelist( ResourceHandle resource_handle, _GPUGraph graph, bool_t do_expensive_check, )[source]#

Extract a the edgelist from a graph

Parameters:

resource_handleResourceHandle: Handle to the underlying device resources needed for referencing data and running algorithms.
graphSGGraph or MGGraph: The input graph.
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 and if applicable
edge_weights, edge_ids and/or edge_type_ids.

Examples

>>> import pylibcugraph, cupy, numpy
>>> srcs = cupy.asarray([0, 1, 1, 2, 2, 2, 3, 4], dtype=numpy.int32)
>>> dsts = cupy.asarray([1, 3, 4, 0, 1, 3, 5, 5], dtype=numpy.int32)
>>> weights = cupy.asarray(
...     [0.1, 2.1, 1.1, 5.1, 3.1, 4.1, 7.2, 3.2], 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=False, renumber=False, do_expensive_check=False)
>>> (sources, destinations, edge_weights, _, _) =
...     pylibcugraph.decompress_to_edgelist(
...         resource_handle, G, False)
>>> sources
[0, 1, 1, 2, 2, 2, 3, 4]
>>> destinations
[1, 3, 4, 0, 1, 3, 5, 5]
>>> edge_weights
[0.1, 2.1, 1.1, 5.1, 3.1, 4.1, 7.2, 3.2]