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.

翻译：社区检测是复杂网络分析中的重要内容。现有的属性网络社区检测方法大多仅利用网络结构，而融合节点属性的方法主要针对传统社区结构，无法检测网络中的多分结构和混合结构。此外，当前针对属性网络提出的基于模型的社区检测方法未充分考虑节点独特的拓扑信息，如介数中心性和聚类系数。因此，本文提出了一种融合节点介数中心性和聚类系数的随机块模型（BCSBM），用于属性网络的社区检测。与其他属性网络生成模型不同，BCSBM模型中的链接和属性生成过程遵循泊松分布，并在随机块模型基础上考虑社区间的连接概率。同时，将节点的介数中心性和聚类系数引入链接与属性的生成过程。最后，采用期望最大化算法估计BCSBM模型参数，并通过硬划分过程获得节点-社区隶属关系，从而完成社区检测。通过在包含不同网络结构的六个真实网络上进行实验，并与五种算法的社区检测结果进行对比，实验结果表明，BCSBM模型不仅继承了随机块模型的优势，能够检测多种网络结构，而且由于引入了节点的介数中心性和聚类系数，具有良好的数据拟合能力。总体而言，该模型的性能优于其他五种对比算法。