Methods for community detection in multi-layer networks

Many complex systems are composed of coupled networks through different layers, where each layer represents one of many possible types of interactions. One of the most relevant features is to extract communities in multi-layer networks. Community detection is a very hard problem and not yet satisfactorily solved, despite has been extensively studied in literature. The current algorithms either collapse multi-layer networks into a single-layer network or extend the algorithms for single-layer networks by using consensus clustering. However, these approaches have been criticized for ignoring the connection among various layers, thereby resulting in low accuracy. To overcome these problems, we propose more methods for community detection that simultaneously take into account multiple layers. Then we compare them through experiments on both artificial and real world networks.