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NotMle: Community Detection in an Inference Way

Learning the node-to-community partition of network (aka. community detection) lies at the core of network analysis, which have proven to be successful for various tasks. How to improve community detection results via using various features is a challenging task. Existing community detection models integrate links and attributes information of each node in a comprehensive manner, towards a single aspect to represent the node's general role. Yet, a real-world entity could be multifaceted, where it connects to different domains due to different motives that are not necessarily correlated. In this paper, we propose a practical framework for community detection based on attribute and structure information for large networks, namely, Non-exhaustive overlapping community detection in attributed networks via two-stage Maximum likelihood estimation, (for short NotMle). The attribute information can be effectively captured and transformed by attribute embedding to encode the combination with structure information. Then, the link strength among communities is designed to adjust the impact of these structural information on community generation based on the contribution of the structure to the clusters, and the node assignment allow for the nature of the real network (overlapping and outliers). Experimental results on several benchmarks demonstrate encouraging results while learning an interpretable latent structure.