SEHG: Bridging Interpretability and Prediction in Self-Explainable Heterogeneous Graph Neural Networks

Zhenhua Huang, Wenhao Zhou, YuFeng Li, Xiuyang Wu, Chengpei Xu, Junfeng Fang, Zhaohong Jia, Linyuan Lü, Feng Xia

Track: Graph algorithms and modeling for the Web

Keywords: Heterogeneous Graph Neural Network, Graph Explanation, Self-Explainable, Graph Self-Supervised Learning

TL;DR: We introduce SEHG, a self-explaible framework that enhances both interpretability and prediction in heterogeneous graph neural networks.

Abstract:

Heterogeneous Graph Neural Networks (HGNNs) are extensively applied in modeling web-based applications that involve heterogeneous graph structures. Explanation models for HGNNs aim to address their "black box" nature. Enhancing the interpretability of HGNNs leads to a better understanding and can potentially improve predictive performance. However, existing post-hoc HGNN explanation methods cannot impact the HGNN's predictions. Self-explainable homogeneous models also perform poorly on heterogeneous graphs.
To address these challenges, we present a Self-Explainable Heterogeneous Graph Neural Network (SEHG), a novel architecture that integrates explanation generation into the learning process of HGNN through two alternative stages. The first stage focuses on producing high-quality explanations while providing predictions alongside. The second stage enhances prediction accuracy by a contrastive learning strategy. Unlike the current methods that rely on manually defined metapaths for structural explanations, SEHG generates important structure and feature explanations by learnable heterogeneous masks. To ensure high-quality and sparsity explanation, these masks are regulated by a uniquely designed range-based penalty during training.
Moreover, we introduce HetBA, a collection of synthetic heterogeneous datasets designed to quantify and visualize explanations or heterogeneous graphs.
Extensive experiments demonstrate the effectiveness of SEHG, which surpasses strong baselines in real-world node classification tasks by notable margins of up to 3.91%. SEHG also achieves state-of-the-art performance on synthetic datasets with improvement of up to 9.44%, and records the highest fidelity scores in explanation tasks, improving by up to 46.57%. To our knowledge, SEHG is a pioneering self-explainable HGNN framework that achieves state-of-the-art performance on both heterogeneous graph explanation and prediction tasks.

Submission Number: 1828