  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.
