cugraph.dask.traversal.bfs.bfs#

cugraph.dask.traversal.bfs.bfs(input_graph, start, depth_limit=None, return_distances=True, check_start=True)[source]#

Find the distances and predecessors for a breadth-first traversal of a graph. The input graph must contain edge list as a dask-cudf dataframe with one partition per GPU.

Parameters:

input_graphcugraph.Graph: cuGraph graph instance, should contain the connectivity information as dask cudf edge list dataframe (edge weights are not used for this algorithm).
startInteger or list or cudf object or dask_cudf object: The id(s) of the graph vertex from which the traversal begins in each component of the graph. Only one vertex per connected component of the graph is allowed.
depth_limitInteger or None, optional (default=None): Limit the depth of the search
return_distancesbool, optional (default=True): Indicates if distances should be returned
check_startbool, optional (default=True): If True, performs more extensive tests on the start vertices to ensure validitity, at the expense of increased run time.

Returns:

dfdask_cudf.DataFrame

df[‘vertex’] gives the vertex id

df[‘distance’] gives the path distance from the starting vertex (Only if return_distances is True)

df[‘predecessor’] gives the vertex it was reached from in the traversal

Examples

>>> import cugraph.dask as dcg
>>> import dask_cudf
>>> # ... Init a DASK Cluster
>>> #    see https://docs.rapids.ai/api/cugraph/stable/dask-cugraph.html
>>> # Download dataset from https://github.com/rapidsai/cugraph/datasets/..
>>> chunksize = dcg.get_chunksize(datasets_path / "karate.csv")
>>> ddf = dask_cudf.read_csv(datasets_path / "karate.csv",
...                          blocksize=chunksize, delimiter=" ",
...                          names=["src", "dst", "value"],
...                          dtype=["int32", "int32", "float32"])
>>> dg = cugraph.Graph(directed=True)
>>> dg.from_dask_cudf_edgelist(ddf, source='src', destination='dst',
...                            edge_attr='value')
>>> df = dcg.bfs(dg, 0)