融合结构-属性交互二部图随机游走的社区搜索方法

属性图中的社区搜索是一种局部社区发现方法,本质是基于用户提供的查询节点返回包含查询节点且在结构内聚的同时属性与查询属性相似的个性化子图.该任务有助于用户更好地理解社区是如何形成的以及社区形成的原因.提出了一种融合结构-属性交互二部图随机游走机制,有效地支持属性图中的社区搜索.具体地,首先基于网络拓扑结构构建结构概率转移矩阵;其次探索结构与属性交互形成的二部图定义2阶段的节点-属性-节点概率转移矩阵,将其与结构概率转移矩阵有效融合得到属性图的概率转移矩阵;最后设计重启随机游走方法,基于融合结构和属性的并行电导值精准查询社区.在真实数据集和人工数据集上的实验表明了本文方法的有效性.

Community search via random walk based on structure-attribute interaction bipartite graph

Community search in attribute graph is a local community detection method, which is essentially based on the query nodes provided by users to return personalized subgraphs containing query nodes with similar attributes of attributes together with structural cohesion. This task helps users better understand how and why communities are formed. This paper proposes a random walk mechanism based on the structure-attribute interaction bipartite graph, which can effectively support the community search in the attribute graph. Specifically, this method first constructs the structure probability transfer matrix based on the network topology, then explores the bipartite graph formed by the interaction of structure and attribute to define the two-stage node-attribute-node probability transfer matrix, and effectively fuses it with the structure probability transfer matrix to obtain the probability transfer matrix of attribute graph. Finally, a random walk with restart method is designed, and the parallel conductance value based on fusion structure and attribute is used to accurately query the community. Experiments on real datasets and artificial data sets verify the effectiveness and efficiency of the proposed method.