MULTILAYER STOCHASTIC BLOCK MODELS FOR COMMUNITY DETECTION IN HETEROGENEOUS NETWORKS

Heterogeneous networks have multiple types of nodes and edges. Single-layer stochastic block model (SBM), bipartite SBM, and multiplex SBM have been proposed as a tool for detecting community structure in networks and generating synthetic networks for use as benchmarks. Yet, any SBM has not been introduced specifically for detecting community in heterogeneous networks. In this paper, we introduce heterogeneous multilayer SBMs for detecting communities in heterogeneous networks. According to these models, we look at heterogeneous networks as multilayer networks, which means each edge type shows one layer. We can categorize these models into two broad groups, those based on the independent degree principle and other based on the shared degree principle. According to our results, in general, the independent degree model has better performance in networks that have less common communities between nodes types. In contrast, the shared degree model has better performance in networks which have more common communities between nodes types. Also, we show that our models outperform in real-world networks. If we put aside the exception case, simulation results and real data applications show the effectiveness of these proposed models compared to single-layer models that are applied to heterogeneous networks.