提出新方法量化节点表示中关系信息的捕捉程度，提升解释可信度。
Rethinking Node Representation Interpretation through Relation Coherence
- 引入节点一致性率（NCI）衡量表示对节点关系的捕捉能力
- 实验显示相较最优旧方法误差降低39%以上
- 适合关注图模型可解释性与无监督表示质量的研究者
理解图模型中的节点表示对于发现偏差、诊断错误和建立模型信任至关重要。然而，以往可解释AI研究多聚焦于解释（预测原因），而非解释（将表示映射到可理解概念）。且现有解释方法缺乏验证，可靠性存疑。本文提出新型解释方法NCI（Node Coherence Rate for Representation Interpretation），量化节点表示中不同关系的捕捉程度，并设计新评估方法IME验证解释准确性。实验表明，NCI使先前最佳方法的平均误差降低39%。进一步应用NCI分析多种图模型的无监督表示，揭示其质量特性。
原文摘要 · Abstract (English)
Understanding node representations in graph-based models is crucial for uncovering biases ,diagnosing errors, and building trust in model decisions. However, previous work on explainable AI for node representations has primarily emphasized explanations (reasons for model predictions) rather than interpretations (mapping representations to understandable concepts). Furthermore, the limited research that focuses on interpretation lacks validation, and thus the reliability of such methods is unclear. We address this gap by proposing a novel interpretation method-Node Coherence Rate for Representation Interpretation (NCI)-which quantifies how well different node relations are captured in node representations. We also propose a novel method (IME) to evaluate the accuracy of different interpretation methods. Our experimental results demonstrate that NCI reduces the error of the previous best approach by an average of 39%. We then apply NCI to derive insights about the node representations produced by several graph-based methods and assess their quality in unsupervised settings.
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