2 research outputs found
Louvain-like Methods for Community Detection in Multi-Layer Networks
In many complex systems, entities interact with each other through
complicated patterns that embed different relationships, thus generating
networks with multiple levels and/or multiple types of edges. When trying to
improve our understanding of those complex networks, it is of paramount
importance to explicitly take the multiple layers of connectivity into account
in the analysis. In this paper, we focus on detecting community structures in
multi-layer networks, i.e., detecting groups of well-connected nodes shared
among the layers, a very popular task that poses a lot of interesting questions
and challenges. Most of the available algorithms in this context either reduce
multi-layer networks to a single-layer network or try to extend algorithms for
single-layer networks by using consensus clustering. Those approaches have
anyway been criticized lately. They indeed ignore the connections among the
different layers, hence giving low accuracy. To overcome these issues, we
propose new community detection methods based on tailored Louvain-like
strategies that simultaneously handle the multiple layers. We consider the
informative case, where all layers show a community structure, and the noisy
case, where some layers only add noise to the system. We report experiments on
both artificial and real-world networks showing the effectiveness of the
proposed strategies.Comment: 16 pages, 4 figure