Higher order heterogeneous graph neural network based on node attribute enhancement

Chao Li, Jinhu Fu, Yeyu Yan, Zhongying Zhao, Qingtian Zeng

Abstract: Heterogeneous graph neural networks (HGNNs) have garnered significant attention owing to their ability to capture attribute information from heterogeneous graphs (HGs). However, practical scenarios involving HGs often suffer from missing node attributes. Furthermore, most existing HGNNs have limitations in exploiting node attributes. Specifically, they cannot entirely capture the attributes of higher order neighbors or only use the higher order homogeneous neighbors, thus disregarding the attributes of heterogeneous neighbors. To address these problems, we propose a higher order heterogeneous graph neural network based on heterogeneous node attribute enhancement (HOAE). We first design an attribute-completion strategy using an advanced transformer based self-attention mechanism to fill in the missing attributes. After that, we propose a simple and efficient attribute enhancement strategy based on heterogeneous attributes, empowering HOAE to fully learn the attributes of heterogeneous neighbors. Additionally, meta-path is incorporated to construct a higher order neighbor-based network, enabling effective learning of higher order attributes. Experimental results on three real world datasets demonstrate that HOAE significantly outperforms state-of-the-art methods. The source code of this work is available at https://github.com/FredJDean/HOAE.