[2210.15304] Explaining the Explainers in Graph Neural Networks: a Comparative Study Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2210.15304 (cs) [Submitted on 27 Oct 2022 ( v1 ), last revised 1 Jul 2024 (this version, v3)] Title: Explaining the Explainers in Graph Neural Networks: a Comparative Study Authors: Antonio Longa , Steve Azzolin , Gabriele Santin , Giulia Cencetti , Pietro Liò , Bruno Lepri , Andrea Passerini View a PDF of the paper titled Explaining the Explainers in Graph Neural Networks: a Comparative Study, by Antonio Longa and 5 other authors View PDF HTML (experimental) Abstract: Following a fast initial breakthrough in graph based learning, Graph Neural Networks (GNNs) have reached a widespread application in many science and engineering fields, prompting the need for methods to understand their decision process. GNN explainers have started to emerge in recent years, with a multitude of methods both novel or adapted from other domains. To sort out this plethora of alternative approaches, several studies have benchmarked the performance of different explainers in terms of various explainability metrics. However, these earlier works make no attempts at providing insights into why different GNN architectures are more or less explainable, or which explainer should be preferred in a given setting. In this survey, we fill these gaps by devising a systematic experimental study, which tests ten explainers on eight representative architectures trained on six carefully designed graph and node classification datasets. With our results we provide key insights on the choice and applicability of GNN explainers, we isolate key components that make them usable and successful and provide recommendations on how to avoid common interpretation pitfalls. We conclude by highlighting open questions and directions of possible future research. Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2210.15304 [cs.LG] (or arXiv:2210.15304v3 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2210.15304 Focus to learn more arXiv-issued DOI via DataCite Journal reference: ACM Comput. Surv. 57, 5, Article 120 (2025), 37 pages Related DOI : https://doi.org/10.1145/3696444 Focus to learn more DOI(s) linking to related resources Submission history From: Antonio Longa [ view email ] [v1] Thu, 27 Oct 2022 10:25:51 UTC (7,276 KB) [v2] Wed, 7 Jun 2023 07:09:58 UTC (7,275 KB) [v3] Mon, 1 Jul 2024 10:48:40 UTC (21,091 KB) Full-text links: Access Paper: View a PDF of the paper titled Explaining the Explainers in Graph Neural Networks: a Comparative Study, by Antonio Longa and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2022-10 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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