Community search over bipartite graphs has attracted significant interest recently. In many applications such as user-item bipartite graph in E-commerce, customer-movie bipartite graph in movie rating website, nodes tend to have attributes, while previous community search algorithm on bipartite graphs ignore attributes, which makes the returned results with poor cohesion with respect to their node attributes. In this paper, we study the community search problem on attributed bipartite graphs. Given a query vertex q, we aim to find attributed $\left(\alpha,\beta\right)$-communities of $G$, where the structure cohesiveness of the community is described by an $\left(\alpha,\beta\right)$-core model, and the attribute similarity of two groups of nodes in the subgraph is maximized. In order to retrieve attributed communities from bipartite graphs, we first propose a basic algorithm composed of two steps: the generation and verification of candidate keyword sets, and then two improved query algorithms Inc and Dec are proposed. Inc is proposed considering the anti-monotonity property of attributed bipartite graphs, then we adopt different generating method and verifying order of candidate keyword sets and propose the Dec algorithm. After evaluating our solutions on eight large graphs, the experimental results demonstrate that our methods are effective and efficient in querying the attributed communities on bipartite graphs.

翻译：二部图上的社区搜索近年来引起了广泛关注。在诸如电子商务中的用户-物品二部图、电影评分网站中的客户-电影二部图等应用中，节点往往具有属性，而以往的二部图社区搜索算法忽略了属性，导致返回结果的节点属性内聚性较差。本文研究了属性二部图上的社区搜索问题。给定查询顶点q，我们旨在找到G中具有属性的(α,β)-社区，其中社区的结构内聚性由(α,β)-核心模型描述，且子图中两组节点的属性相似度最大化。为了从二部图中检索属性社区，我们首先提出一个由两步组成的基础算法：候选关键词集的生成与验证，然后提出两种改进查询算法Inc和Dec。Inc算法考虑了属性二部图的反单调性质，随后我们采用不同的候选关键词集生成方法和验证顺序，提出Dec算法。在八个大型图上评估我们的解决方案后，实验结果表明，我们的方法在二部图上查询属性社区时既有效又高效。