Variational community partition with novel network structure centrality prior

In this paper, we proposed a novel two-stage optimization method for network community partition, which is based on inherent network structure information. The introduced optimization approach utilizes the new centrality measure of both network links and vertices to construct the key affinity description of the given network, while the direct similarities between graph nodes or nodal features are not available to obtain the classical affinity matrix. Indeed, such calculated network centrality information not only encodes the essential hints for detecting meaningful network communities, but also introduces a 'confidence' criterion for referencing new labeled benchmark nodes. For the resulted challenging combinatorial optimization problem of graph clustering, the proposed optimization method iteratively employs an efficient convex optimization algorithm which is developed based under a new variational perspective of primal and dual. Experiments over both artificial and real-world network datasets demonstrate that the proposed optimization strategy of community partition significantly improves results and outperforms the state-of-the-art algorithms in terms of accuracy and robustness, while using much less annotated benchmark information. (C) 2019 Published by Elsevier Inc.