GRExplainer: A Universal Explanation Method for Temporal Graph Neural Networks

TL;DR

GRExplainer introduces a universal, efficient, and user-friendly explanation method for temporal graph neural networks, capable of handling various TGNN types and reducing computational costs while improving interpretability.

Contribution

It is the first explanation method for TGNNs that is universal, efficient, and does not require prior knowledge, applicable to different TGNN architectures.

Findings

Outperforms baseline methods in generality and efficiency.

Works effectively on six real-world datasets.

Provides automated, continuous explanations using RNN-based generative models.

Abstract

Dynamic graphs are widely used to represent evolving real-world networks. Temporal Graph Neural Networks (TGNNs) have emerged as a powerful tool for processing such graphs, but the lack of transparency and explainability limits their practical adoption. Research on TGNN explainability is still in its early stages and faces several key issues: (i) Current methods are tailored to specific TGNN types, restricting generality. (ii) They suffer from high computational costs, making them unsuitable for large-scale networks. (iii) They often overlook the structural connectivity of explanations and require prior knowledge, reducing user-friendliness. To address these issues, we propose GRExplainer, the first universal, efficient, and user-friendly explanation method for TGNNs. GRExplainer extracts node sequences as a unified feature representation, making it independent of specific input formats and thus applicable to both snapshot-based and event-based TGNNs (the major types of TGNNs). By utilizing breadth-first search and temporal information to construct input node sequences, GRExplainer reduces redundant computation and improves efficiency. To enhance user-friendliness, we design a generative model based on Recurrent Neural Networks (RNNs), enabling automated and continuous explanation generation. Experiments on six real-world datasets with three target TGNNs show that GRExplainer outperforms existing baseline methods in generality, efficiency, and user-friendliness.