pylibcugraph.ego_graph#

pylibcugraph.ego_graph( ResourceHandle resource_handle, _GPUGraph graph, source_vertices, size_t radius, bool_t do_expensive_check, )[source]#

Compute the induced subgraph of neighbors centered at nodes source_vertices, within a given radius.

Parameters:

resource_handleResourceHandle: Handle to the underlying device resources needed for referencing data and running algorithms.
graphSGGraph or MGGraph: The input graph.
source_verticescupy array: The centered nodes from which the induced subgraph will be extracted
radius: size_t: The number of hops to go out from each source vertex
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 the subgraph_offsets(if there are more than one seeds)

Examples

>>> import pylibcugraph, cupy, numpy
>>> srcs = cupy.asarray([0, 1, 1, 2, 2, 2, 3, 3, 4], dtype=numpy.int32)
>>> dsts = cupy.asarray([1, 3, 4, 0, 1, 3, 4, 5, 5], dtype=numpy.int32)
>>> weights = cupy.asarray(
...     [0.1, 2.1, 1.1, 5.1, 3.1, 4.1, 7.2, 3.2, 6.1], dtype=numpy.float32)
>>> source_vertices = cupy.asarray([0, 1], 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, weight_array=weights,
...     store_transposed=False, renumber=False, do_expensive_check=False)
>>> (sources, destinations, edge_weights, subgraph_offsets) =
...     pylibcugraph.ego_graph(resource_handle, G, source_vertices, 2, False)
>>> sources
[0, 1, 1, 3, 1, 1, 3, 3, 4]
>>> destinations
[1, 3, 4, 4, 3, 4, 4, 5, 5]
>>> edge_weights
[0.1, 2.1, 1.1, 7.2, 2.1, 1.1, 7.2, 3.2, 6.1]
>>> subgraph_offsets
[0, 4, 9]