提出基于节点度的边依赖噪声模型，更真实模拟图数据中的标签错误。
EDN: A Novel Edge-Dependent Noise Model for Graph Data
- 构建三种边依赖噪声模型，标签错误率随节点度变化。
- 实验显示两种EDN变体使GNN和抗噪算法性能下降更显著。
- 适合评估图学习模型鲁棒性，尤其关注真实场景噪声建模。
图数据的重要结构特征是其边集，它们捕捉了节点间的关联关系（图拓扑）。现有节点标签噪声模型如对称标签噪声（SLN）和类别条件噪声（CCN）忽略了图数据中节点间的关系。本文提出的边依赖噪声（EDN）模型弥补了这一缺陷，认为现实场景中标签噪声可能受节点连接关系影响。我们探索了三种EDN变体，发现所有变体中节点标签被污染的概率均与其度相关。进一步比较不同变体下该概率与节点度的关系。我们在5种主流GNN架构和8种图数据抗噪算法上，在多个常用图数据集上进行实验。结果表明，两种EDN变体导致的性能退化远超传统标签噪声模型。通过设定合理的假设检验问题，我们进行了统计验证。这凸显了在评估图学习抗噪算法时引入EDN的重要性，以提升复杂噪声环境下图学习的可靠性。
原文摘要 · Abstract (English)
An important structural feature of a graph is its set of edges, as it captures the relationships among the nodes (the graph's topology). Existing node label noise models like Symmetric Label Noise (SLN) and Class Conditional Noise (CCN) disregard this important node relationship in graph data; and the Edge-Dependent Noise (EDN) model addresses this limitation. EDN posits that in real-world scenarios, label noise may be influenced by the connections between nodes. We explore three variants of EDN. A crucial notion that relates nodes and edges in a graph is the degree of a node; we show that in all three variants, the probability of a node's label corruption is dependent on its degree. Additionally, we compare the dependence of these probabilities on node degree across different variants. We performed experiments on popular graph datasets using 5 different GNN architectures and 8 noise robust algorithms for graph data. The results demonstrate that 2 variants of EDN lead to greater performance degradation in both Graph Neural Networks (GNNs) and existing noise-robust algorithms, as compared to traditional node label noise models. We statistically verify this by posing a suitable hypothesis-testing problem. This emphasizes the importance of incorporating EDN when evaluating noise robust algorithms for graphs, to enhance the reliability of graph-based learning in noisy environments.
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