A Variance-aware Multiobjective Louvain-like Method for Community Detection in Multiplex Networks

TL;DR

This paper introduces a variance-aware multiobjective Louvain-like method for community detection in multiplex networks, optimizing community detection across multiple layers without aggregating layer information.

Contribution

It generalizes the Louvain method by incorporating variance and multiobjective optimization, enabling more robust community detection across network layers.

Findings

Effective in synthetic and real-world networks

Robust to noisy layers

Outperforms traditional aggregation methods

Abstract

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.