Temporal Graph Neural Networks (TGNNs), crucial for modeling dynamic graphs with time-varying interactions, face a significant challenge in explainability due to their complex model structure. Counterfactual explanations, crucial for understanding model decisions, examine how input graph changes affect outcomes. This paper introduces two novel counterfactual explanation methods for TGNNs: GreeDy (Greedy Explainer for Dynamic Graphs) and CoDy (Counterfactual Explainer for Dynamic Graphs). They treat explanations as a search problem, seeking input graph alterations that alter model predictions. GreeDy uses a simple, greedy approach, while CoDy employs a sophisticated Monte Carlo Tree Search algorithm. Experiments show both methods effectively generate clear explanations. Notably, CoDy outperforms GreeDy and existing factual methods, with up to 59\% higher success rate in finding significant counterfactual inputs. This highlights CoDy's potential in clarifying TGNN decision-making, increasing their transparency and trustworthiness in practice.

该论文介绍了两种新颖的TGNN（Temporal Graph Neural Networks）反事实解释方法：GreeDy（动态图的贪婪解释器）和CoDy（动态图的反事实解释器）。实验证明，这两种方法能够有效生成清晰的解释。特别是，CoDy在寻找显著的反事实输入方面优于GreeDy和现有的事实方法，其成功率高达59％。这凸显了CoDy在澄清TGNN决策过程中的潜力，提高了它们在实践中的透明度和可靠性。

贪婪和协同：用于动态图的反事实解释器