cugraph.ecg#

cugraph.ecg(input_graph, min_weight: float = 0.0001, ensemble_size: int = 100, max_level: int = 10, threshold: float = 1e-07, resolution: float = 1.0, random_state: Optional[int] = None, 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.
max_levelinteger, optional (default=100): This controls the maximum number of levels/iterations of the ECG algorithm. When specified the algorithm will terminate after no more than the specified number of iterations. No error occurs when the algorithm terminates early in this manner.
threshold: float: Modularity gain threshold for each level. If the gain of modularity between 2 levels of the algorithm is less than the given threshold then the algorithm stops and returns the resulting communities. Defaults to 1e-7.
resolution: float, optional (default=1.0): Called gamma in the modularity formula, this changes the size of the communities. Higher resolutions lead to more smaller communities, lower resolutions lead to fewer larger communities. Defaults to 1.
random_state: int, optional(default=None): Random state to use when generating samples. Optional argument, defaults to a hash of process id, time, and hostname.
weightstr, optional (default=None): Deprecated. 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.

parts[vertex]cudf.Series: Contains the vertex identifiers
parts[partition]cudf.Series: Contains the partition assigned to the vertices

modularity_scorefloat

A floating point number containing the global modularity score of the partitioning.

Examples

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