Extracting the mesoscopic structure from heterogeneous systems

Heterogeneous systems in nature are often characterized by the mesoscopic structure known as communities. In this paper, we propose a framework to address the problem of community detection in bipartite networks and tripartite hypernetworks, which are appropriate models for many heterogeneous systems. The most important advantage of our method is that it is competent for detecting both communities of one-to-one correspondence and communities of many-to-many correspondence, while state of the art techniques can only handle the former. We demonstrate this advantage and show other desired properties of our method through extensive experiments in both synthetic and real-world datasets.