Hierarchical Clustering Based on Hyper-edge Similarity for Community Detection

Community structure is very important for many real-world networks. It has been shown that communities are overlapping and hierarchical. However, most previous methods, based on the graph model, can´t investigate these two properties of community structure simultaneously. Moreover, in some cases the use of simple graphs does not provide a complete description of the real-world network. After introducing hyper graphs to describe real-world networks and defining hyper-edge similarity measurement, we propose a Hierarchical Clustering method based on Hyper-edge Similarity (HCHS) to simultaneously detect both the overlapping and hierarchical properties of complex community structure, as well as using the newly introduced community density to evaluate the goodness of a community. The examples of application to real-world networks give excellent results.