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arXiv · 2209.00807

An Explainer for Temporal Graph Neural Networks

Wenchong He·Minh N. Vu·Zhe Jiang·My T. Thai
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Abstract

Temporal graph neural networks (TGNNs) have been widely used for modeling time-evolving graph-related tasks due to their ability to capture both graph topology dependency and non-linear temporal dynamic. The explanation of TGNNs is of vital importance for a transparent and trustworthy model. However, the complex topology structure and temporal dependency make explaining TGNN models very challenging. In this paper, we propose a novel explainer framework for TGNN models. Given a time series on a graph to be explained, the framework can identify dominant explanations in the form of a probabilistic graphical model in a time period. Case studies on the transportation domain demonstrate that the proposed approach can discover dynamic dependency structures in a road network for a time period.

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Wenchong He, Minh N. Vu, Zhe Jiang, My T. Thai. 2022-09-02. An Explainer for Temporal Graph Neural Networks. https://arxiv.org/abs/2209.00807

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