pylibcugraph.node2vec

pylibcugraph.node2vec(ResourceHandle resource_handle, _GPUGraph graph, seed_array, size_t max_depth, bool_t compress_result, double p, double q)

Computes random walks under node2vec sampling procedure.

Parameters:

resource_handleResourceHandle

Handle to the underlying device resources needed for referencing data and running algorithms.

graphSGGraph

The input graph.

seed_array: device array type

Device array containing the pointer to the array of seed vertices.

max_depthsize_t

Maximum number of vertices in generated path

compress_resultbool_t

If true, the paths are unpadded and a third return device array contains the sizes for each path, otherwise the paths are padded and the third return device array is empty.

pdouble

The return factor p represents the likelihood of backtracking to a node in the walk. A higher value (> max(q, 1)) makes it less likely to sample a previously visited node, while a lower value (< min(q, 1)) would make it more likely to backtrack, making the walk more “local”.

qdouble

The in-out factor q represents the likelihood of visiting nodes closer or further from the outgoing node. If q > 1, the random walk is likelier to visit nodes closer to the outgoing node. If q < 1, the random walk is likelier to visit nodes further from the outgoing node.

Returns:

A tuple of device arrays, where the first item in the tuple is a device

array containing the compressed paths, the second item is a device

array containing the corresponding weights for each edge traversed in

each path, and the third item is a device array containing the sizes

for each of the compressed paths, if compress_result is True.

Examples

>>> import pylibcugraph, cupy, numpy
>>> srcs = cupy.asarray([0, 1, 2], dtype=numpy.int32)
>>> dsts = cupy.asarray([1, 2, 3], dtype=numpy.int32)
>>> seeds = cupy.asarray([0, 0, 1], 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=False, renumber=False, do_expensive_check=False)
>>> (paths, weights, sizes) = pylibcugraph.node2vec(
...                             resource_handle, G, seeds, 3, True, 1.0, 1.0)