A network embedding-enhanced Bayesian model for generalized community detection in complex networks

Abstract Community detection has been a significant and long-standing task in network analysis . While existing methods tend to focus on the assortative structure where communities are densely connected internally, in reality other kinds of network structure (e.g., disassortative) also exist. In addition, previous methods often have difficulties in dealing with noise and redundant information in the network topology . To address these problems, we develop a novel Bayesian probabilistic model for identifying the generalized communities, regardless of whether the network structure is assortative, disassortative, or otherwise. Specifically, the model combines the original adjacency matrix representation with the network embedding (the dense and continuous vector representations of nodes in a low-dimensional space). The two parts are connected and generated jointly via the community memberships of nodes learned from the model. Finally, we take a Bayesian treatment for model parameters and develop an efficient variational inference algorithm to detect communities. Experimental results demonstrate the outstanding performance of the new approach both on synthetic networks and on real-world networks, while case studies validate the ability of the proposed approach to describe generalized communities meaningfully.