Researchers have developed two new model-agnostic explainers for Temporal Graph Neural Networks (TGNNs), utilizing Shapley and Owen values. These methods aim to make the predictions of TGNNs, which combine spatial and temporal data, more interpretable. The explainers have demonstrated superior performance compared to existing state-of-the-art methods on various datasets and revealed a flaw in a common TGAT implementation regarding timestamp extraction. AI
影响 Enhances interpretability of TGNNs, potentially improving model debugging and trustworthiness.
排序理由 Academic paper on explainability methods for Temporal Graph Neural Networks.
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