pylibcugraph.weakly_connected_components#

pylibcugraph.weakly_connected_components(ResourceHandle resource_handle, _GPUGraph graph, offsets, indices, weights, labels, bool_t do_expensive_check)[source]#

Generate the Weakly Connected Components from either an input graph or or CSR arrays(‘offsets’, ‘indices’, ‘weights’) and attach a component label to each vertex.

Parameters:
resource_handleResourceHandle

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

graphSGGraph or MGGraph

The input graph.

offsetsobject supporting a __cuda_array_interface__ interface

Array containing the offsets values of a Compressed Sparse Row matrix that represents the graph.

indicesobject supporting a __cuda_array_interface__ interface

Array containing the indices values of a Compressed Sparse Row matrix that represents the graph.

weightsobject supporting a __cuda_array_interface__ interface

Array containing the weights values of a Compressed Sparse Row matrix that represents the 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 containing containing two device arrays which are respectively
vertices and their corresponding labels

Examples

>>> import pylibcugraph, cupy, numpy
>>> from pylibcugraph import weakly_connected_components
>>> srcs = cupy.asarray([0, 1, 1, 2, 2, 0], dtype=numpy.int32)
>>> dsts = cupy.asarray([1, 0, 2, 1, 0, 2], dtype=numpy.int32)
>>> weights = cupy.asarray(
...     [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], dtype=numpy.float32)
>>> resource_handle = pylibcugraph.ResourceHandle()
>>> graph_props = pylibcugraph.GraphProperties(
...      is_symmetric=True, is_multigraph=False)
>>> G = pylibcugraph.SGGraph(
...     resource_handle, graph_props, srcs, dsts, weights,
...     store_transposed=False, renumber=True, do_expensive_check=False)
>>> (vertices, labels) = weakly_connected_components(
...     resource_handle, G, None, None, None, None, False)
>>> vertices
[0, 1, 2]
>>> labels
[2, 2, 2]
>>> import cupy as cp
>>> import numpy as np
>>> from scipy.sparse import csr_matrix
>>>
>>> graph = [
... [0, 1, 1, 0, 0],
... [0, 0, 1, 0, 0],
... [0, 0, 0, 0, 0],
... [0, 0, 0, 0, 1],
... [0, 0, 0, 0, 0],
... ]
>>> scipy_csr = csr_matrix(graph)
>>> rows, cols = scipy_csr.nonzero()
>>> scipy_csr[cols, rows] = scipy_csr[rows, cols]
>>>
>>> cp_offsets = cp.asarray(scipy_csr.indptr)
>>> cp_indices = cp.asarray(scipy_csr.indices, dtype=np.int32)
>>>
>>> resource_handle = pylibcugraph.ResourceHandle()
>>> weakly_connected_components(resource_handle=resource_handle,
                                graph=None,
...                             offsets=cp_offsets,
...                             indices=cp_indices,
...                             weights=None,
...                             False)
>>> print(f"{len(set(cp_labels.tolist()))} - {cp_labels}")
2 - [2 2 2 4 4]