arXiv:2605.22385v1 Announce Type: new Abstract: Explaining graph neural networks (GNNs) has become more and more important recently. Higher-order interpretation schemes, such as GNN-LRP (layer-wise relevance propagation for GNN), emerged as powerful tools for unraveling how different features interact thereby contributing to explaining GNNs. GNN-LRP gives a relevance attribution of walks between nodes at each layer, and the subgraph attribution is expressed as a sum over exponentially many such walks. In this work, we demonstrate that such exponential complexity can be avoided. In particular,
The increasing complexity and opacity of Graph Neural Networks (GNNs) necessitates more efficient and robust explainability methods, with this research building on prior work like GNN-LRP.
Improved explainability for GNNs is crucial for their adoption in high-stakes applications, as it provides transparency into their decision-making processes, which is a major barrier to wider implementation.
This research suggests a method to make higher-order GNN explanations computationally feasible, potentially enabling broader and more practical use of explainable GNNs.
- · AI researchers
- · Machine learning explainability platforms
- · Industries using GNNs (e.g., drug discovery, fraud detection)
- · Opaque AI systems
- · Inefficient GNN explanation methods
More widespread deployment of GNNs in critical applications due to enhanced interpretability.
Increased trust in AI systems that leverage GNN architectures, accelerating their integration into sensitive domains.
Potential for new regulatory frameworks for AI that mandate explainability, influenced by the availability of practical tools.
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Read at arXiv cs.LG