Self-supervised Graph Neural Network Based Community Search over Heterogeneous Information Networks

Abstract

Community search in heterogeneous information network (CSH) based on deep learning methods has received increasing attention. However, almost all the existing methods are semi-supervised learning paradigms, and the learning models based on meta path only consider the end-to-end relationship of meta path, ignoring the intermediate information of meta path. To address these issues, a CSH method based on Self-supervised Graph Neural Network (SGNN) is proposed. The model training is self-supervised by contrastive learning between the network schema view and the meta path view, and the two views capture the local and global information of the meta path from different angles. We then introduce a greedy algorithm called $k{\text{-}}core$ and ${\mathcal{K}}{\text{-}}sized$ attribute-scores maximization community search ($k{\mathcal{K}}{\text{ - ASMcs}}$) to explore target communities. A large number of experiments on real datasets have verified the effectiveness and efficiency of the proposed method.