Evaluating Link Prediction Explanations for Graph Neural Networks

• URL: http://arxiv.org/abs/2308.01682v1
• Date: Thu, 3 Aug 2023 10:48:37 GMT
• Title: Evaluating Link Prediction Explanations for Graph Neural Networks
• Authors: Claudio Borile, Alan Perotti, Andr\'e Panisson
• Abstract summary: We provide metrics to assess the quality of link prediction explanations, with or without ground-truth. We discuss how underlying assumptions and technical details specific to the link prediction task, such as the choice of distance between node embeddings, can influence the quality of the explanations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph Machine Learning (GML) has numerous applications, such as node/graph classification and link prediction, in real-world domains. Providing human-understandable explanations for GML models is a challenging yet fundamental task to foster their adoption, but validating explanations for link prediction models has received little attention. In this paper, we provide quantitative metrics to assess the quality of link prediction explanations, with or without ground-truth. State-of-the-art explainability methods for Graph Neural Networks are evaluated using these metrics. We discuss how underlying assumptions and technical details specific to the link prediction task, such as the choice of distance between node embeddings, can influence the quality of the explanations.

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