社区有助于揭示复杂网络结构和个体间的关系.研究人员从不同视角提出很多社区发现方法,用来识别团内紧密、团间稀疏的网络结构.自2006年以来,提出了一些基于统计推理的社区发现方法,它们可识别实际网络中更多的潜在结构,并以其可靠的理论基础和优越的结构识别能力成为当前的主流.该类方法的主要目标是建立符合实际网络的生成模型以拟合观测网络,将社区发现问题转化为贝叶斯推理问题.首先给出社区发现中生成模型的相关定义；其次按照模型中社区组成元素将已有统计推理模型分为节点社区推理模型和链接社区推理模型,并深入探讨各种模型的设计思想及实现算法；再次,总结各模型适用的网络类型及规模、发现的社区结构、算法复杂度等,给出一种选择已有基于统计推理的社区发现模型的方法,并利用基准数据集对已有典型统计推理模型进行验证及分析；最后探讨了基于统计推理模型的社区发现存在的主要问题和未来发展的方向.%Community detection can identify salient structure and relations among individuals from the complex network. Researchers put forward many different methods, which are mainly used to detect the groups with dense connections within groups but sparser connections between them. To detect more latent structures in reality networks, various models on statistical inference have been proposed since 2006, which are on sound theoretical principles and have better performances identifying structures, and have become the state-of-the-art models. These models' aims are to define a generative process to fit the observed network, and transfer the community detecting problem to Bayesian inference. First,the concepts on generation model were defined. Then,the article divided the generation models on community detection into vertex community and link community based on composition in community, and discussed design ideas and algorithms of each model in detail. What these models adapt to was also summarized from aspects of network type and scale,community structure, complexity etc,and then a method was given on how to select an existed statistical model. The existing classical models were tested and analyzed on the popular benchmark datasets. In the end,main problems on these models were highlighted, as well as the future progress.
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