  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 network centrality
measure of both links and vertices to construct the key affinity description of
the given network, where the direct similarities between graph nodes or nodal
features are not available to obtain the classical affinity matrix. Indeed,
such calculated network centrality information presents the essential structure
of network, hence, the proper measure for detecting network communities, which
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 detection significantly improves result accuracy and outperforms the
state-of-the-art algorithms in terms of accuracy and reliability.
