nnsed(g_original: object, dimensions: int = 32, iterations: int = 10, seed: int = 42) → cdlib.classes.node_clustering.NodeClustering¶
The procedure uses non-negative matrix factorization in order to learn an unnormalized cluster membership distribution over nodes. The method can be used in an overlapping and non-overlapping way.
Supported Graph Types
Undirected Directed Weighted Yes No No Parameters:
- g_original – a networkx/igraph object
- dimensions – Embedding layer size. Default is 32.
- iterations – Number of training epochs. Default 10.
- seed – Random seed for weight initializations. Default 42.
>>> from cdlib import algorithms >>> import networkx as nx >>> G = nx.karate_club_graph() >>> coms = algorithms.nnsed(G)
Sun, Bing-Jie, et al. “A non-negative symmetric encoder-decoder approach for community detection.” Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 2017.
Reference implementation: https://karateclub.readthedocs.io/