Overlapping Community Detection in Weighted Graphs: Matrix Factorization Approach
This work investigates the overlapping community detection problem. Recently, some efficient matrix factorization algorithms were proposed which can detect overlapping communities in unweighted graphs with millions of nodes. We expand these approaches to weighted graphs and develop a novel probabilistic model of overlapping community structure in weighted graphs. The resulting algorithm boils down to generalized matrix factorization with non-quadratic loss function. The comparison with the other methods shows that the proposed algorithm outperforms modern analogues.
KeywordsOverlapping community detection Social graphs Matrix factorization
The reported study was funded by RFBR according to the research project 18-37-00489.
- 2.Backstrom, L., Huttenlocher, D., Kleinberg, J., Lan, X.: Group formation in large social networks: membership, growth, and evolution. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 44–54. ACM (2006). https://doi.org/10.1145/1150402.1150412
- 7.Krogan, N.J., et al.: Global landscape of protein complexes in the yeast saccharomyces cerevisiae. Nature 440(7084), 637–643 (2006). http://www.nature.com/nature/journal/v440/n7084/full/nature04670.htmlCrossRefGoogle Scholar
- 14.Yang, J., Leskovec, J.: Overlapping community detection at scale: a nonnegative matrix factorization approach. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp. 587–596. ACM (2013). https://doi.org/10.1145/2433396.2433471