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New algorithms improve GNN explainability by reducing walk search complexity

By PulseAugur Editorial · [2 sources] ·

Researchers have developed new polynomial-time algorithms to address the exponential computational complexity of identifying relevant walks in Graph Neural Networks (GNNs). This advancement significantly improves the applicability of GNN-LRP, a method for explaining GNNs by analyzing information flows through walks. The proposed algorithms, based on the max-product method, enable exact and approximate identification of top-K relevant walks, demonstrating effectiveness across various benchmarks including epidemiology, molecular, and natural language tasks. AI

IMPACT Enhances the interpretability and trustworthiness of GNNs, potentially increasing their adoption in safety-critical applications.

RANK_REASON The cluster contains an academic paper detailing new algorithms for improving the explainability of Graph Neural Networks.

Read on arXiv cs.LG →

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

COVERAGE [2]

  1. arXiv cs.LG TIER_1 · Ping Xiong, Thomas Schnake, Michael Gastegger, Gr\'egoire Montavon, Klaus-Robert M\"uller, Shinichi Nakajima ·

    Relevant Walk Search for Explaining Graph Neural Networks

    arXiv:2605.23673v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have become important machine learning tools for graph analysis, and its explainability is crucial for safety, fairness, and robustness. Layer-wise relevance propagation for GNNs (GNN-LRP) evaluates the …

  2. arXiv cs.LG TIER_1 · Shinichi Nakajima ·

    Relevant Walk Search for Explaining Graph Neural Networks

    Graph Neural Networks (GNNs) have become important machine learning tools for graph analysis, and its explainability is crucial for safety, fairness, and robustness. Layer-wise relevance propagation for GNNs (GNN-LRP) evaluates the relevance of \emph{walks} to reveal important in…

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