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语义社会网络是一种由信息节点及社会关系构成的新型复杂网络,而传统社会网络社区发现算法以节点邻接关系为挖掘对象,因此无法有效处理语义社会网络重叠社区发现问题.针对这一问题,提出基于语义数据场的语义重叠社区发现算法,该算法首先以LDA(latent dirichlet allocat,ion)模型为语义信息模型,利用Gibbs取样法建立节点语义信息到语义空间的量化映射;其次,利用节点间语义坐标及链接关系,建立节点的语义数据场模型;再次,以语义关系强度及语义势能为参数,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;最后提出可度量语义社区发现结果的语义模块度模型.通过实验分析,验证了本文算法及语义模块度模型的有效性及可行性.
Semantic social network is a new type of complex network composed of information nodes and social relations. However, the traditional social network community discovery algorithm takes node adjacency as the mining object, so it can not effectively deal with the problem of overlapping community discovery of the semantic social network.Aiming at this problem, This paper proposes a semantic overlapped community discovery algorithm based on semantic data fields. This algorithm firstly uses LDA model as semantic information model and uses Gibbs sampling method to establish the quantitative mapping of semantic information to semantic space. Secondly, Semantic data and semantic relationship of semantic data, and establish the semantic data field model of node.Secondly, based on the semantic relationship strength and semantic potential energy, an improved random walk strategy for overlapping community discovery of semantic social networks is proposed. Finally, The result of the semantic module degree model.Through experimental analysis, the effectiveness and feasibility of the algorithm and the semantic modularity model is verified.