mlsanityData to 14 Oct 2026

Evaluating Neighbor Explainability for Graph Neural Networks

arXiv 2311.08118 ↗First posted 14 Nov 2023cs.LG cs.AIcited by 1

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Abstract (arXiv)

Explainability in Graph Neural Networks (GNNs) is a new field growing in the last few years. In this publication we address the problem of determining how important is each neighbor for the GNN when classifying a node and how to measure the performance for this specific task. To do this, various known explainability methods are reformulated to get the neighbor importance and four new metrics are presented. Our results show that there is almost no difference between the explanations provided by gradient-based techniques in the GNN domain. In addition, many explainability techniques failed to identify important neighbors when GNNs without self-loops are used.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 27 of its 39 references that are in the map. First posted on arXiv 14 Nov 2023.

Builds on

27 of 39 references.

References · 27

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15 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Parallelizing Node-Level Explainability in Graph Neural Networkscited by 0