节点不对称转移概率的网络社区发现算法

社区发现是当前社会网络研究领域的一个热点和难点，现有的研究方法包括：（1）优化以网络拓扑结构为基础的社区质量指标；（2）评估节点间的相似性并进行聚类；（3）根据特定网络设计相应的社区模型等.这些方法存在如下问题：（1）通用性不高，难以同时在无向网络和有向网络上发挥出好的效果；（2）无法充分利用网络的结构信息，在真实数据集上表现不佳.针对上述问题，提出一种基于节点不对称转移概率的网络社区发现算法CDATP.该算法通过分析网络拓扑结构来设计节点转移概率，并使用random walk方法评估节点对网络社区的重要性.最后，以重要性较高的节点作为核心构造网络社区.与现有的基于random walk的方法不同，CDATP为网络中节点设计的转移概率具有不对称性，并只通过节点局部转移来评估节点对社区的重要程度.通过大量仿真实验表明，CDATP在人工模拟数据集和真实数据集上均比其他最新算法有更好的表现.

Community Detection Algorithm Based on Asymmetric Transition Probability of Nodes

Community detection is a popular and difficult problem in the field of social network analysis. Most of the current researches mainly focus on optimizing the modularity index, evaluating the similarity of nodes, and designing different models to fit particular networks. These approaches usually suffer from following problems:(1) just a few of them can deal with directed networks as well as undirected networks; and (2) real-world networks being more complex than synthetic networks, many community detection strategies cannot perform well in real-world networks. To solve these problems, this paper presents an algorithm for community detection in complex networks based on random walk method. Different from existing methods based on random walk method, the asymmetric transition probability is designed for the nodes according to network topology and other information. The event propagation law is also applied to the evaluation of nodes importance. The algorithm CDATP performs well on both real-world networks and synthetic networks.