Addressing the Scarcity of Benchmarks for Graph XAI

• URL: http://arxiv.org/abs/2505.12437v1
• Date: Sun, 18 May 2025 14:19:52 GMT
• Title: Addressing the Scarcity of Benchmarks for Graph XAI
• Authors: Michele Fontanesi, Alessio Micheli, Marco Podda, Domenico Tortorella,
• Abstract summary: We propose a general method to automate the construction of XAI benchmarks for graph classification from real-world datasets.<n>We provide both 15 ready-made benchmarks, as well as the code to generate more than 2000 additional XAI benchmarks with our method.
• Score: 6.387263468033964
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While Graph Neural Networks (GNNs) have become the de facto model for learning from structured data, their decisional process remains opaque to the end user, undermining their deployment in safety-critical applications. In the case of graph classification, Explainable Artificial Intelligence (XAI) techniques address this major issue by identifying sub-graph motifs that explain predictions. However, advancements in this field are hindered by a chronic scarcity of benchmark datasets with known ground-truth motifs to assess the explanations' quality. Current graph XAI benchmarks are limited to synthetic data or a handful of real-world tasks hand-curated by domain experts. In this paper, we propose a general method to automate the construction of XAI benchmarks for graph classification from real-world datasets. We provide both 15 ready-made benchmarks, as well as the code to generate more than 2000 additional XAI benchmarks with our method. As a use case, we employ our benchmarks to assess the effectiveness of some popular graph explainers.

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