提出新方法提升符号图神经网络的链接符号预测性能
DropEdge not Foolproof: Effective Augmentation Method for Signed Graph Neural Networks
- 设计结构增强模块识别潜在边，筛选有益增广候选
- 在Slashdot数据集上使SGCN的F1-micro提升32.3%
- 适合研究符号图、图神经网络增广的学者使用
该论文研究符号图，即用正负边表示友好或敌对关系，聚焦于链接符号预测任务。尽管符号图神经网络（SGNNs）已取得进展，但仍面临图稀疏性和不平衡三角形等问题。现有数据增强方法大多不适用于符号图，因缺乏辅助信息。作者指出，虽有少数适用的随机删边（DropEdge）方法，但其无法提升预测性能。为此，提出符号图增广（SGA）框架，包含结构增广模块以识别候选边，并设计选择策略保留有益增广，从而显著改善SGNN训练效果。实验表明，SGA在Slashdot数据集上使SGCN的F1-micro提升32.3%，显著优于基线。
原文摘要 · Abstract (English)
The paper discusses signed graphs, which model friendly or antagonistic relationships using edges marked with positive or negative signs, focusing on the task of link sign prediction. While Signed Graph Neural Networks (SGNNs) have advanced, they face challenges like graph sparsity and unbalanced triangles. The authors propose using data augmentation (DA) techniques to address these issues, although many existing methods are not suitable for signed graphs due to a lack of side information. They highlight that the random DropEdge method, a rare DA approach applicable to signed graphs, does not enhance link sign prediction performance. In response, they introduce the Signed Graph Augmentation (SGA) framework, which includes a structure augmentation module to identify candidate edges and a strategy for selecting beneficial candidates, ultimately improving SGNN training. Experimental results show that SGA significantly boosts the performance of SGNN models, with a notable 32.3% improvement in F1-micro for SGCN on the Slashdot dataset.
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