Community search for multiple nodes on attribute graphs

Abstract

Community search is individualized and targeted, which identifies local communities containing query nodes and meeting specific conditions only according to users. Community search is only interested in communities that satisfy the designated criteria, and has a wide range of application scenarios in the real world. Traditional community search only uses the topology information, without considering the impact of node attributes information. And many existing algorithms can only deal with a single query node. Therefore, this paper defines the attribute community search problem of multiple query nodes on attribute graphs, which utilizes the edge connectivity as the structural tightness constraint and designs an attribute function as the attribute tightness constraint. To solve this problem, we design a framework that firstly finds the maximum k edge connected-component containing query as a candidate subgraph, then iteratively deletes the node with the least contribution to the attribute function from the candidate subgraph and adjusts the subgraph according to the structural constraints. We also design the attribute Steiner distance that synthesizes the structural information and attribute information, and propose an efficient heuristic expansion algorithm based on distance. The experimental results on several datasets show our proposed methods can find the community containing all query nodes with high tightness of structure and homogeneity of attributes.