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 relevance of \emph{walks} to reveal important information flows in the network, and provides higher-order explanations, which have been shown to be superior to the lower-order, i.e., node-/edge-level, explanations. However, identifying relevant walks by GNN-LRP requires {\em exponential} computational complexity with r
The increasing adoption of GNNs in critical applications necessitates improved explainability, and this research addresses a fundamental computational barrier to more robust explanations.
Improved explainability in GNNs is crucial for ensuring safety, fairness, and trustworthiness, particularly as AI systems become more autonomous and integrated into sensitive domains.
This research moves towards making higher-order, more comprehensive explanations for GNN decisions computationally feasible, potentially unlocking new classes of auditable and deployable GNN applications.
- · AI developers
- · Regulatory bodies
- · Adopters of GNN technology
- · Academic researchers in AI explainability
- · Developers relying on black-box GNN deployments
- · Applications where explainability is currently difficult to achieve
More sophisticated and computationally efficient methods for GNN explainability become accessible.
Increased trust and adoption of GNNs in high-stakes fields like finance, healthcare, and autonomous systems due to enhanced transparency.
New regulatory frameworks and standards for AI explainability may emerge, impacting the design and deployment of future AI systems.
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Read at arXiv cs.LG