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Researchers develop Shapley value explainers for temporal graph neural networks

By PulseAugur Editorial · [2 sources] ·

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

IMPACT Enhances interpretability of TGNNs, potentially improving model debugging and trustworthiness.

RANK_REASON Academic paper on explainability methods for Temporal Graph Neural Networks.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Researchers develop Shapley value explainers for temporal graph neural networks

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Lea-Marie Sussek, Stefan Heindorf ·

    Explaining Temporal Graph Predictions With Shapley Values

    arXiv:2604.24078v1 Announce Type: new Abstract: Temporal Graph Neural Networks (TGNNs) have become increasingly popular in recent years due to their superior predictive performance by combining both spatial and temporal information. However, how these models utilize the informati…

  2. arXiv cs.LG TIER_1 English(EN) · Stefan Heindorf ·

    Explaining Temporal Graph Predictions With Shapley Values

    Temporal Graph Neural Networks (TGNNs) have become increasingly popular in recent years due to their superior predictive performance by combining both spatial and temporal information. However, how these models utilize the information to make predictions is rather unexplored, lea…

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