def ecg(input_graph, min_weight=0.05, ensemble_size=16):
"""
Compute the Ensemble Clustering for Graphs (ECG) partition of the input
graph. ECG runs truncated Louvain on an ensemble of permutations of the
input graph, then uses the ensemble partitions to determine weights for
the input graph. The final result is found by running full Louvain on
the input graph using the determined weights.
See https://arxiv.org/abs/1809.05578 for further information.
Parameters
----------
input_graph : cugraph.Graph
cuGraph graph descriptor, should contain the connectivity information
and weights. The adjacency list will be computed if not already
present.
min_weight : floating point
The minimum value to assign as an edgeweight in the ECG algorithm.
It should be a value in the range [0,1] usually left as the default
value of .05
ensemble_size : integer
The number of graph permutations to use for the ensemble.
The default value is 16, larger values may produce higher quality
partitions for some graphs.
Returns
-------
parts : cudf.DataFrame
GPU data frame of size V containing two columns, the vertex id and
the partition id it is assigned to.
df[vertex] : cudf.Series
Contains the vertex identifiers
df[partition] : cudf.Series
Contains the partition assigned to the vertices
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='2')
>>> parts = cugraph.ecg(G)
"""
parts = ecg_wrapper.ecg(input_graph, min_weight, ensemble_size)
if input_graph.renumbered:
parts = input_graph.unrenumber(parts, "vertex")
return parts
def ecg(input_graph, min_weight=0.05, ensemble_size=16, weight=None):
"""
Compute the Ensemble Clustering for Graphs (ECG) partition of the input
graph. ECG runs truncated Louvain on an ensemble of permutations of the
input graph, then uses the ensemble partitions to determine weights for
the input graph. The final result is found by running full Louvain on
the input graph using the determined weights.
See https://arxiv.org/abs/1809.05578 for further information.
Parameters
----------
input_graph : cugraph.Graph or NetworkX Graph
The graph descriptor should contain the connectivity information
and weights. The adjacency list will be computed if not already
present.
min_weight : float, optional (default=0.5)
The minimum value to assign as an edgeweight in the ECG algorithm.
It should be a value in the range [0,1] usually left as the default
value of .05
ensemble_size : integer, optional (default=16)
The number of graph permutations to use for the ensemble.
The default value is 16, larger values may produce higher quality
partitions for some graphs.
weight : str, optional (default=None)
This parameter is here for NetworkX compatibility and
represents which NetworkX data column represents Edge weights.
Returns
-------
parts : cudf.DataFrame or python dictionary
GPU data frame of size V containing two columns, the vertex id and
the partition id it is assigned to.
df[vertex] : cudf.Series
Contains the vertex identifiers
df[partition] : cudf.Series
Contains the partition assigned to the 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='2')
>>> parts = cugraph.ecg(G)
"""
input_graph, isNx = ensure_cugraph_obj_for_nx(input_graph, weight)
parts = ecg_wrapper.ecg(input_graph, min_weight, ensemble_size)
if input_graph.renumbered:
parts = input_graph.unrenumber(parts, "vertex")
if isNx is True:
return df_score_to_dictionary(parts, 'partition')
else:
return parts