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In this paper, we proposed a novel two-stage optimization method for networkcommunity partition, which is based on inherent network structure information.The introduced optimization approach utilizes the new network centralitymeasure of both links and vertices to construct the key affinity description ofthe given network, where the direct similarities between graph nodes or nodalfeatures are not available to obtain the classical affinity matrix. Indeed,such calculated network centrality information presents the essential structureof network, hence, the proper measure for detecting network communities, whichalso introduces a `confidence' criterion for referencing new labeled benchmarknodes. For the resulted challenging combinatorial optimization problem of graphclustering, the proposed optimization method iteratively employs an efficientconvex optimization algorithm which is developed based under a new variationalperspective of primal and dual. Experiments over both artificial and real-worldnetwork datasets demonstrate that the proposed optimization strategy ofcommunity detection significantly improves result accuracy and outperforms thestate-of-the-art algorithms in terms of accuracy and reliability.

Variational Community Partition with Novel Network Structure Centrality Prior