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Overlapping Community Detection in Weighted Graphs: Matrix Factorization Approach

  • Konstantin Slavnov
  • Maxim PanovEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 794)

Abstract

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.

Keywords

Overlapping community detection Social graphs Matrix factorization 

Notes

Acknowledgments

The reported study was funded by RFBR according to the research project 18-37-00489.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Skolkovo Institute of Science and Technology (Skoltech)MoscowRussia
  2. 2.Higher School of EconomicsMoscowRussia

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