  Community detection is an important content in complex network analysis. The
existing community detection methods in attributed networks mostly focus on
only using network structure, while the methods of integrating node attributes
is mainly for the traditional community structures, and cannot detect
multipartite structures and mixture structures in network. In addition, the
model-based community detection methods currently proposed for attributed
networks do not fully consider unique topology information of nodes, such as
betweenness centrality and clustering coefficient. Therefore, a stochastic
block model that integrates betweenness centrality and clustering coefficient
of nodes for community detection in attributed networks, named BCSBM, is
proposed in this paper. Different from other generative models for attributed
networks, the generation process of links and attributes in BCSBM model follows
the Poisson distribution, and the probability between community is considered
based on the stochastic block model. Moreover, the betweenness centrality and
clustering coefficient of nodes are introduced into the process of links and
attributes generation. Finally, the expectation maximization algorithm is
employed to estimate the parameters of the BCSBM model, and the node-community
memberships is obtained through the hard division process, so the community
detection is completed. By experimenting on six real-work networks containing
different network structures, and comparing with the community detection
results of five algorithms, the experimental results show that the BCSBM model
not only inherits the advantages of the stochastic block model and can detect
various network structures, but also has good data fitting ability due to
introducing the betweenness centrality and clustering coefficient of nodes.
Overall, the performance of this model is superior to other five compared
algorithms.
