Machine learning models that operate on graph-structured data, such as molecular graphs or social networks, often make accurate predictions but offer little insight into why certain predictions are made. Counterfactual explanations address this challenge by seeking the closest alternative scenario where the model's prediction would change. Although counterfactual explanations are extensively studied in tabular data and computer vision, the graph domain remains comparatively underexplored. Constructing graph counterfactuals is intrinsically difficult because graphs are discrete and non-euclidean objects. We introduce Graph Diffusion Counterfactual Explanation, a novel framework for generating counterfactual explanations on graph data, combining discrete diffusion models and classifier-free guidance. We empirically demonstrate that our method reliably generates in-distribution as well as minimally structurally different counterfactuals for both discrete classification targets and continuous properties.

翻译：在分子图或社交网络等图结构数据上运行的机器学习模型通常能做出准确预测，但很少提供关于为何做出特定预测的见解。反事实解释通过寻找模型预测发生改变的最接近替代场景来解决这一挑战。尽管反事实解释在表格数据和计算机视觉领域已得到广泛研究，但在图领域仍相对探索不足。构建图反事实本质上具有挑战性，因为图是离散且非欧几里得的对象。本文提出图扩散反事实解释，这是一种结合离散扩散模型和无分类器引导的新型框架，用于生成图数据的反事实解释。我们通过实验证明，该方法能可靠地生成符合数据分布且结构差异最小的反事实，适用于离散分类目标和连续属性。