Communities unfolding in multislice networks

TL;DR

This paper introduces a modularity-based community detection method for multislice networks, including a network size reduction technique to improve efficiency while maintaining effectiveness.

Contribution

It presents a novel approach combining network size reduction with an approximation algorithm for community detection in multislice networks.

Findings

The method effectively detects communities in multislice networks.

Network size reduction preserves modularity and improves computational performance.

The approach maintains high accuracy in community detection.

Abstract

Discovering communities in complex networks helps to understand the behaviour of the network. Some works in this promising research area exist, but communities uncovering in time-dependent and/or multiplex networks has not deeply investigated yet. In this paper, we propose a communities detection approach for multislice networks based on modularity optimization. We first present a method to reduce the network size that still preserves modularity. Then we introduce an algorithm that approximates modularity optimization (as usually adopted) for multislice networks, thus finding communities. The network size reduction allows us to maintain acceptable performances without affecting the effectiveness of the proposed approach.