cugraph.ecg#

cugraph.ecg(input_graph, min_weight=0.05, ensemble_size=16, weight=None)[source]#

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_graphcugraph.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_weightfloat, 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_sizeinteger, 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.
weightstr, optional (default=None): This parameter is here for NetworkX compatibility and represents which NetworkX data column represents Edge weights.

Returns:

partscudf.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

>>> from cugraph.datasets import karate
>>> G = karate.get_graph(download=True)
>>> parts = cugraph.ecg(G)