def weakly_connected_components(G):
"""
Generate the weakly connected components and attach a component label to
each vertex.
Parameters
----------
G : cugraph.Graph
cuGraph graph descriptor, should contain the connectivity information
as an edge list (edge weights are not used for this algorithm).
Currently, the graph should be undirected where an undirected edge is
represented by a directed edge in both directions. The adjacency list
will be computed if not already present. The number of vertices should
fit into a 32b int.
Returns
-------
df : cudf.DataFrame
df['labels'][i] gives the label id of the i'th vertex
df['vertices'][i] gives the vertex id of the i'th vertex
Examples
--------
>>> M = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>> dtype=['int32', 'int32', 'float32'], header=None)
>>> sources = cudf.Series(M['0'])
>>> destinations = cudf.Series(M['1'])
>>> G = cugraph.Graph()
>>> G.add_edge_list(sources, destinations, None)
>>> df = cugraph.weakly_connected_components(G)
"""
df = connectivity_wrapper.weakly_connected_components(G)
return df
def weakly_connected_components(G):
"""
Generate the Weakly Connected Components and attach a component label to
each vertex.
Parameters
----------
G : cugraph.Graph or networkx.Graph
cuGraph graph descriptor, should contain the connectivity information
as an edge list (edge weights are not used for this algorithm).
Currently, the graph should be undirected where an undirected edge is
represented by a directed edge in both directions. The adjacency list
will be computed if not already present. The number of vertices should
fit into a 32b int.
Returns
-------
df : cudf.DataFrame
GPU data frame containing two cudf.Series of size V: the vertex
identifiers and the corresponding component identifier.
df['vertices']
Contains the vertex identifier
df['labels']
The component identifier
Examples
--------
>>> M = cudf.read_csv('datasets/karate.csv',
delimiter = ' ',
dtype=['int32', 'int32', 'float32'],
header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(M, source='0', destination='1', edge_attr=None)
>>> df = cugraph.weakly_connected_components(G)
"""
G, isNx = check_nx_graph(G)
df = connectivity_wrapper.weakly_connected_components(G)
if G.renumbered:
df = G.unrenumber(df, "vertices")
if isNx is True:
df = df_score_to_dictionary(df, "labels", "vertices")
return df
def weakly_connected_components(G,
directed=None,
connection=None,
return_labels=None):
"""
Generate the Weakly Connected Components and attach a component label to
each vertex.
Parameters
----------
G : cugraph.Graph, networkx.Graph, CuPy or SciPy sparse matrix
Graph or matrix object, which should contain the connectivity
information (edge weights are not used for this algorithm). If using a
graph object, the graph can be either directed or undirected where an
undirected edge is represented by a directed edge in both directions.
The adjacency list will be computed if not already present. The number
of vertices should fit into a 32b int.
directed : bool, optional
NOTE
For non-Graph-type (eg. sparse matrix) values of G only.
Raises TypeError if used with a Graph object.
If True (default), then convert the input matrix to a cugraph.DiGraph
and only move from point i to point j along paths csgraph[i, j]. If
False, then find the shortest path on an undirected graph: the
algorithm can progress from point i to j along csgraph[i, j] or
csgraph[j, i].
connection : str, optional (default=None)
Added for SciPy compatibility, can only be specified for non-Graph-type
(eg. sparse matrix) values of G only (raises TypeError if used with a
Graph object), and can only be set to "weak" for this API.
return_labels : bool, optional
NOTE
For non-Graph-type (eg. sparse matrix) values of G only. Raises
TypeError if used with a Graph object.
If True (default), then return the labels for each of the connected
components.
Returns
-------
Return value type is based on the input type. If G is a cugraph.Graph,
returns:
cudf.DataFrame
GPU data frame containing two cudf.Series of size V: the vertex
identifiers and the corresponding component identifier.
df['vertex']
Contains the vertex identifier
df['labels']
The component identifier
If G is a networkx.Graph, returns:
python dictionary, where keys are vertices and values are the component
identifiers.
If G is a CuPy or SciPy matrix, returns:
CuPy ndarray (if CuPy matrix input) or Numpy ndarray (if SciPy matrix
input) of shape (<num vertices>, 2), where column 0 contains component
identifiers and column 1 contains vertices.
Examples
--------
>>> M = cudf.read_csv(datasets_path / 'karate.csv',
... delimiter = ' ',
... dtype=['int32', 'int32', 'float32'],
... header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(M, source='0', destination='1', edge_attr=None)
>>> df = cugraph.weakly_connected_components(G)
"""
(directed, connection,
return_labels) = _ensure_args("weakly_connected_components", G, directed,
connection, return_labels)
# FIXME: allow nx_weight_attr to be specified
(G, input_type) = ensure_cugraph_obj(
G,
nx_weight_attr="weight",
matrix_graph_type=DiGraph if directed else Graph)
df = connectivity_wrapper.weakly_connected_components(G)
if G.renumbered:
df = G.unrenumber(df, "vertex")
return _convert_df_to_output_type(df, input_type, return_labels)