为图数据中的每个节点打分，揭示模型在不同节点上的表现差异。
Draw a Portrait of Your Graph Data: An Instance-Level Profiling Framework for Graph-Structured Data
- 基于特征、标签和结构信息，给每个节点生成可解释的诊断分数。
- 即使整体准确率相同，也能发现模型在不同节点上的系统性差异。
- 适用于检测知识图谱中的错误或语义不一致节点。
图机器学习模型虽然整体性能相近，但在节点层面的行为却存在差异，对不同节点的可靠性各不相同。标准评估指标如准确率会掩盖这些细粒度差异，难以定位模型失效的具体情况。我们提出 NodePro，一个节点级诊断框架，通过为每个节点分配可解释的评分，实现对模型行为的细粒度分析。该评分融合数据相关信号（如特征差异、标签不确定性、结构模糊性）与模型相关指标（如预测置信度和训练一致性）。通过将模型行为与这些评分对齐，NodePro 能揭示即使在聚合指标无差别的条件下，模型间的系统性差异。我们证明了节点评分可推广至未见节点，无需真实标签即可预测可靠性。最后，我们在结构化知识图谱中验证了 NodePro 在识别语义不一致或损坏节点方面的有效性，展示了其在真实场景中的实用性。
原文摘要 · Abstract (English)
Graph machine learning models often achieve similar overall performance yet behave differently at the node level, failing on different subsets of nodes with varying reliability. Standard evaluation metrics such as accuracy obscure these fine grained differences, making it difficult to diagnose when and where models fail. We introduce NodePro, a node profiling framework that enables fine-grained diagnosis of model behavior by assigning interpretable profile scores to individual nodes. These scores combine data-centric signals, such as feature dissimilarity, label uncertainty, and structural ambiguity, with model-centric measures of prediction confidence and consistency during training. By aligning model behavior with these profiles, NodePro reveals systematic differences between models, even when aggregate metrics are indistinguishable. We show that node profiles generalize to unseen nodes, supporting prediction reliability without ground-truth labels. Finally, we demonstrate the utility of NodePro in identifying semantically inconsistent or corrupted nodes in a structured knowledge graph, illustrating its effectiveness in real-world settings.
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