CS-DAHIN: Community Search Over Dynamic Attribute Heterogeneous Network

Community search (CS) is an important research topic in network analysis, which aims to find a subgraph that satisfies the given conditions. A dynamic attribute heterogeneous information network (DAHIN) is a sequence of attribute heterogeneous information network (AHIN) snapshots, where each snapshot consists of multiple types of vertices as well as edges, and each vertex is associated a set of attribute keywords. CS over DAHIN faces many challenges. In this paper, we study the CS problem over DAHINs, aiming to search for cohesive subgraphs containing query vertex and simultaneously satisfying the connectivity, attribute cohesiveness and interaction stability. To this end, we propose a deep learning model with a three-level attention mechanism and the concept of interaction frequency with respect to multiple semantic relationships to measure the similarity of attributes and the stability of interactions between vertices respectively. In addition, we design three search algorithms to locate the target community by optimizing the degree of interaction stability and attribute similarity between vertices. Extensive experiments, including comparison with existing algorithms, ablation analysis, parameter sensitivity examination, and case studies, are conducted on four real-world datasets to validate the effectiveness and efficiency of the proposed model and search algorithms. The code and model of CS-DAHIN will be open source on GitHub.