cdlib.algorithms.edmot

edmot(g_original: object, component_count: int = 2, cutoff: int = 10) → cdlib.classes.node_clustering.NodeClustering

The algorithm first creates the graph of higher order motifs. This graph is clustered by the Louvain method.

Supported Graph Types

Undirected	Directed	Weighted
Yes	No	No

Parameters:

• g_original – a networkx/igraph object
• component_count – Number of extracted motif hypergraph components. Default is 2.
• cutoff – Motif edge cut-off value. Default is 10.

Returns:

NodeClustering object

Example:

>>> from cdlib import algorithms
>>> import networkx as nx
>>> G = nx.karate_club_graph()
>>> coms = algorithms.edmot(G, max_loop=1000)

References:

Li, Pei-Zhen, et al. “EdMot: An Edge Enhancement Approach for Motif-aware Community Detection.” Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019.

Note

Reference implementation: https://karateclub.readthedocs.io/