  In this paper, we focus on the community detection problem in multiplex
networks, i.e., networks with multiple layers having same node sets and no
inter-layer connections. In particular, we look for groups of nodes that can be
recognized as communities consistently across the layers. To this end, we
propose a new approach that generalizes the Louvain method by (a)
simultaneously updating average and variance of the modularity scores across
the layers, and (b) reformulating the greedy search procedure in terms of a
filter-based multiobjective optimization scheme. Unlike many previous
modularity maximization strategies, which rely on some form of aggregation of
the various layers, our multiobjective approach aims at maximizing the
individual modularities on each layer simultaneously. We report experiments on
synthetic and real-world networks, showing the effectiveness and the robustness
of the proposed strategies both in the informative case, where all layers show
the same community structure, and in the noisy case, where some layers
represent only noise.
