Communities unfolding in multislice networks

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.

8 pages, 4 figures

References in corpus (5)

• Fast unfolding of communities in large networks
• Benchmark graphs for testing community detection algorithms
• Quantifying social group evolution
• Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities
• Size reduction of complex networks preserving modularity

Cited by in corpus (5)

• Multilayer Network Science: from Cells to Societies
• A Method for Group Extraction and Analysis in Multilayer Social Networks
• Understanding Co-evolution in Large Multi-relational Social Networks
• Mining Essential Relationships under Multiplex Networks
• Node-centric community detection in multilayer networks with layer-coverage diversification bias