Centrality-based Relation aware Heterogeneous Graph Neural Network

Abstract

The representation of heterogeneous graph nodes has become a hot research topic due to its diverse applications. However, extant approaches can only give consideration partly to three aspects: node structure, semantics and features. To better integrate these three aspects, a new semi-supervised graph neural network is proposed in this paper, called the CRHGNN (Centrality-based Relation aware Heterogeneous Graph Neural Network). The CRHGNN consists of four components as follows. The first component performs the encoding work and aims to capture the structure and semantics of the nodes. The second and third components perform attention mechanisms and information aggregation, respectively. The CRHGNN learns the mutual attention between nodes and carries out feature learning with less overfitting and fewer oversmoothing problems. The last component performs relation fusion, aiming to obtain a compact representation of the nodes. Experiments are conducted to evaluate the representation learning of nodes on three real-world heterogeneous graph datasets and demonstrate that the proposed model is very competitive in terms of node classification and node clustering tasks.