基于随机游走的语义重叠社区发现算法

语义社会网络是由信息节点及社会关系构成的一类新型复杂网络，因此语义社会网络重叠社区发现是传统社区发现研究的新方向.针对这一问题，提出基于随机游走的语义社会网络重叠社区发现算法，该算法首先以LDA(latent Dirichlet allocation)算法为基础建立语义空间，实现节点语义信息到语义空间的量化映射；其次，以语义空间中节点信息熵作为节点语义信息比重，以节点的度分布比率作为节点关系比重，建立节点语义影响力模型及语义社会网络的加权邻接矩阵；再次，以语义影响力模型和加权邻接矩阵为参数，提出一种改进的语义社会网络重叠社区发现的随机游走策略，并提出可度量语义社区发现结果的语义模块度模型；最后，通过实验分析，验证了所提出的算法及语义模块度模型的有效性和可行性.

A Semantic Overlapping Community Detecting Algorithm in Social Networks Based on Random Walk

Since the semantic social networks (SSN) is a new kind of complex networks, the community detection is a new investigation relevant to the traditional community detection research. To solve this problem, an overlapping community structure detecting method in semantic social network is proposed based on the random walk strategy. The algorithm establishes the semantic space using latent Dirichlet allocation (LDA) method. Firstly, the quantization mapping is completed by which semantic information in nodes can be changed into the semantic space. Secondly, the semantic influence model and weighed adjacent matrix of SSN are established, with the entropy of nodes in SSN as the semantic information proportion, the distribution ratio of nodes as the weight of adjacent. Thirdly, an improved random walk strategy of community structure detecting in overlapping-SSN is proposed, with the distribution ratio of nodes as parameter, and a semantic modularity model is proposed by which the community structure of SSN can be measured. Finally, the efficiency and feasibility of the proposed algorithm and the semantic modularity are verified by experimental analysis.