NAEx让网络对齐结果可解释，自动找出关键子图和特征。
NAEx: A Plug-and-Play Framework for Explaining Network Alignment
- 通过可学习的边和特征掩码联合建模结构与特征空间。
- 在四个基准模型上实现高效且忠实的解释，提升可比性。
- 适合需要信任高风险场景（如生物、社交）的研究者。
网络对齐（NA）旨在识别多个网络间的对应节点，应用于社交网络、合作者关系及生物学等领域。尽管对齐模型不断进步，其可解释性仍受限，难以理解对齐决策，影响在高风险领域建立信任。为此，我们提出NAEx，一个即插即用、模型无关的解释框架，通过识别影响预测的关键子图和特征来解释对齐模型。NAEx通过联合参数化图结构与特征空间（使用可学习的边和特征掩码），并引入优化目标，确保解释既忠实于原始预测，又能有效比较跨网络的结构与特征相似性。该框架为归纳式设计，能高效生成未见数据的对齐解释。我们还提出了针对对齐可解释性的评估指标，并在基准数据集上集成四种代表性NA模型，验证了NAEx的有效性与效率。
原文摘要 · Abstract (English)
Network alignment (NA) identifies corresponding nodes across multiple networks, with applications in domains like social networks, co-authorship, and biology. Despite advances in alignment models, their interpretability remains limited, making it difficult to understand alignment decisions and posing challenges in building trust, particularly in high-stakes domains. To address this, we introduce NAEx, a plug-and-play, model-agnostic framework that explains alignment models by identifying key subgraphs and features influencing predictions. NAEx addresses the key challenge of preserving the joint cross-network dependencies on alignment decisions by: (1) jointly parameterizing graph structures and feature spaces through learnable edge and feature masks, and (2) introducing an optimization objective that ensures explanations are both faithful to the original predictions and enable meaningful comparisons of structural and feature-based similarities between networks. NAEx is an inductive framework that efficiently generates NA explanations for previously unseen data. We introduce evaluation metrics tailored to alignment explainability and demonstrate NAEx's effectiveness and efficiency on benchmark datasets by integrating it with four representative NA models.
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