big_clam(g_original: object, dimensions: int = 8, iterations: int = 50, learning_rate: float = 0.005) → cdlib.classes.node_clustering.NodeClustering¶
BigClam is an overlapping community detection method that scales to large networks. The procedure uses gradient ascent to create an embedding which is used for deciding the node-cluster affiliations.
Supported Graph Types
Undirected Directed Weighted Yes No No Parameters:
- g_original – a networkx/igraph object
- dimensions – Number of embedding dimensions. Default 8.
- iterations – Number of training iterations. Default 50.
- learning_rate – Gradient ascent learning rate. Default is 0.005.
>>> from cdlib import algorithms >>> import networkx as nx >>> G = nx.karate_club_graph() >>> coms = algorithms.big_clam(G)
Yang, Jaewon, and Jure Leskovec. “Overlapping community detection at scale: a nonnegative matrix factorization approach.” Proceedings of the sixth ACM international conference on Web search and data mining. 2013.
Reference implementation: https://karateclub.readthedocs.io/