HENCE-X: Toward Heterogeneity-Agnostic Multi-Level Explainability for Deep Graph Networks

作者：Ge Lv, Chen Zhang, Lei Chen · 发表于：Proceedings of the VLDB Endowment · 年份：2023 · DOI：10.14778/3611479.3611503 · 被引用次数：5 · 研究领域：Advanced Graph Neural Networks、Explainable Artificial Intelligence (XAI)、Bayesian Modeling and Causal Inference

Deep graph networks (DGNs) have demonstrated their outstanding effectiveness on both heterogeneous and homogeneous graphs. However their black-box nature does not allow human users to understand their working mechanisms. Recently, extensive efforts have been devoted to explaining DGNs' prediction, yet heterogeneity-agnostic multi-level explainability is still less explored. Since the two types of graphs are both irreplaceable in real-life applications, having a more general and end-to-end explainer becomes a natural and inevitable choice. In the meantime, feature-level explanation is often ignored by existing techniques, while topological-level explanation alone can be incomplete and deceptive. Thus, we propose a heterogeneity-agnostic multi-level explainer in this paper, named HENCE-X, which is a causality-guided method that can capture the non-linear dependencies of model behavior on the input using conditional probabilities. We theoretically prove that HENCE-X is guaranteed to find the Markov blanket of the explained prediction, meaning that all information that the prediction is dependent on is identified. Experiments on three real-world datasets show that HENCE-X outperforms state-of-the-art (SOTA) methods in generating faithful factual and counterfactual explanations of DGNs.