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：针对二分图的社区搜索问题近年来引起了广泛关注。在许多实际应用中，如电子商务中的用户-商品二分图、电影评分网站中的客户-电影二分图，节点通常具有属性特征，而现有的二分图社区搜索算法忽略了属性信息，导致返回结果在节点属性方面缺乏内聚性。本文研究了属性二分图上的社区搜索问题。给定查询顶点$q$，我们旨在寻找图$G$中的属性$(\alpha,\beta)$-社区，其中社区的结构凝聚性由$(\alpha,\beta)$-核模型描述，且子图中两组节点的属性相似度最大化。为从二分图中检索属性社区，我们首先提出一种由两个步骤组成的基础算法：候选关键词集的生成与验证；随后提出两种改进查询算法Inc和Dec。Inc算法利用了属性二分图的反单调性，而Dec算法则通过采用不同的候选关键词集生成方法与验证顺序来设计。我们在八个大规模图上评估了所提方案，实验结果表明，这些方法在二分图上查询属性社区时具有高效性和有效性。