发表机构
Institute of Computer Science, Foundation for Research and Technology - Hellas (FORTH); University of Crete（希腊研究与技术基金会计算机科学研究所; 克里特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究对比六种SOTA图神经网络反事实解释器，在多类图与节点分类任务数据集上，用多指标评估其在解释大小、覆盖率等方面的表现，为该领域研究提供指导。
AI 中文摘要
针对图结构化数据的反事实解释旨在确定输入图所需的最小且符合实际的修改，以将模型的预测结果更改为预定义的输出。尽管最近出现了支持通过添加和删除边来修改图的反事实解释器，但仍然缺乏通用且高效的方法，尤其是在考虑生成解释的质量时。此外，该问题仍远未解决，因为现有方法各有优缺点，通常在解释大小、覆盖率和质量之间进行权衡。因此，确定每种方法的适用场景和不足，以指导该领域的未来研究十分重要。为此，本研究在涵盖二分类和多分类图及节点分类任务的多样化真实世界和合成数据集上，对比了六种最先进（SOTA）模型，并使用多种定量和定性指标评估它们的性能。
英文摘要
Counterfactual explanations for graph-structured data seek to determine minimal and realistic modifications required in an input graph to alter a model's prediction to a predefined output. Although counterfactual explainers that support modifying the graph by both adding and removing edges have recently emerged, there is still a lack of general and efficient methods, especially when considering the quality of the generated explanations. Moreover, the problem remains far from solved, as existing methods exhibit different strengths and weaknesses, often trading off between explanation size, coverage and quality. For this reason, it is important to identify where each method performs well and where it falls short, so as to guide future research in the field. Thus, our study compares six state-of-the-art (SOTA) models on a diverse set of real-world and synthetic datasets, covering both binary and multi-class graph and node classification tasks, and evaluates their performance using diverse quantitative and qualitative metrics.
