Efficient Bayesian Community Detection using Non-negative Matrix Factorisation

Abstract

Identifying overlapping communities in networks is a challenging task. In this work we present a novel approach to community detection that utilises the Bayesian non-negative matrix factorisation (NMF) model to produce a probabilistic output for node memberships. The scheme has the advantage of computational efficiency, soft community membership and an intuitive foundation. We present the performance of the method against a variety of benchmark problems and compare and contrast it to several other algorithms for community detection. Our approach performs favourably compared to other methods at a fraction of the computational costs.

References in corpus (7)

• Fast unfolding of communities in large networks
• Modularity and community structure in networks
• Uncovering the overlapping community structure of complex networks in nature and society
• Resolution limit in community detection
• Comparing community structure identification
• Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities
• Weighted network modules