Efficient community search with multiple semantic relationships in heterogeneous information networks

Abstract:

To address key limitations in existing community search methods for heterogeneous information networks (HINs) , this paper proposes a community search method for HINs that integrates multiple semantic relationships, employing an efficient “offline learning, online search” strategy. Its core lies in: adaptively learning the weight contributions of different meta-paths to the target community cohesiveness using a semantic attention mechanism to precisely quantify semantic differences; and subsequently measuring node relevance by combining network structure and node attribute features to locate community members. In the offline phase, a node-community association model is pre-trained to generate probability distribution vectors indicating node affiliation across various communities. In the online phase, community search is rapidly responded to based on precomputed results. This strategy maintains the flexibility of the learning model to effectively capture heterogeneous network semantics and attributes, while shifting the main computational burden to the offline phase, significantly improving query efficiency, making it particularly suitable for high-query-frequency scenarios. Experiments on multiple real-world HIN datasets demonstrate that our method significantly outperforms existing mainstream methods in both community effectiveness (semantic relevance, structural cohesiveness, attribute consistency) and query efficiency.