Weighted k-core community search on heterogeneous information networks

Abstract

Community search is a widely used technique in graph data mining that aims to find communities containing a given query node. While existing works have mainly focused on homogeneous information networks, most real-world networks are heterogeneous. To address this, this paper proposes a weighted k-core community search method designed for heterogeneous information networks. Firstly, the influence of the association weight between nodes based on meta-paths on the community search results is considered, and a weighted k-core community model (k, P)-Wcore is established, thereby improving the accuracy of community search. Subsequently, in order to improve search efficiency, an optimization algorithm OptWcore based on graph traversal search space is designed. This algorithm can effectively reduce redundant calculations and reduce the depth of path search, thereby improving search efficiency. Finally, experiments conducted on four real-world heterogeneous information network datasets demonstrate the effectiveness and efficiency of the proposed method.