Paper Info
Title
Multilayer Modularity Belief Propagation To Assess Detectability Of Community Structure
Abstract
Modularity-based community detection encompasses a number of widely used, efficient heuristics for identification of structure in networks. Recently, a belief propagation approach to modularity optimization provided a useful guide for identifying nontrivial structure in single-layer networks in a way that other optimization heuristics have not. In this paper, we extend modularity belief propagation to multilayer networks. As part of this development, we also directly incorporate a resolution parameter. We show that adjusting the resolution parameter affects the convergence properties of the algorithm and yields different community structures than the baseline. We compare our approach with a widely used community detection tool, GenLouvain, across a range of synthetic, multilayer benchmark networks, demonstrating that our method performs comparably to the state of the art. Finally, we demonstrate the practical advantages of the additional information provided by our tool by way of two real-world network examples. We show how the convergence properties of the algorithm can be used in selecting the appropriate resolution and coupling parameters and how the node-level marginals provide an interpretation for the strength of attachment to the identified communities. We have released our tool as a Python package for convenient use.
Year
DOI
Venue
2020
10.1137/19M1279812
SIAM JOURNAL ON MATHEMATICS OF DATA SCIENCE
Keywords
DocType
Volume
community detection, modularity, belief propagation, networks, multilayer networks, message passing, resolution parameter
Journal
2
Issue
Citations 
PageRank 
3
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
William H Weir100.34
Benjamin Walker200.34
Lenka Zdeborová3119078.62
Peter J Mucha400.34