通过边平衡增强提升对比链接预测
Enhancing Contrastive Link Prediction With Edge Balancing Augmentation
AI总结：
针对对比学习在链接预测中缺乏理论分析和未充分考虑节点度的问题，本文首次提供相关理论分析并提出边平衡增强（EBA）方法，结合新对比损失形成CoEBA，在8个基准数据集上显著优于现有模型。
摘要：
链接预测是图挖掘中最基础的任务之一，这推动了近期利用对比学习提升性能的研究。然而，我们发现这些研究存在两个主要弱点：i）缺乏针对链接预测中对比学习的理论分析；ii）在对比学习中对节点度的考虑不足。为解决上述问题，我们首次为链接预测中的对比学习提供了正式的理论分析，分析结果可推广到结合对比学习的基于自动编码器的链接预测模型。基于分析结果，我们提出一种新的图增强方法——边平衡增强（EBA），该方法通过调整节点度实现图增强。随后，我们提出一种新方法，名为结合边平衡增强的对比链接预测（CoEBA），其整合了所提出的EBA与新的对比损失以提升模型性能。我们在8个基准数据集上进行了实验，结果表明我们提出的CoEBA显著优于其他最先进的链接预测模型。
英文摘要：
Link prediction is one of the most fundamental tasks in graph mining, which motivates the recent studies of leveraging contrastive learning to enhance the performance. However, we observe two major weaknesses of these studies: i) the lack of theoretical analysis for contrastive learning on link prediction, and ii) inadequate consideration of node degrees in contrastive learning. To address the above weaknesses, we provide the first formal theoretical analysis for contrastive learning on link prediction, where our analysis results can generalize to the autoencoder-based link prediction models with contrastive learning. Motivated by our analysis results, we propose a new graph augmentation approach, Edge Balancing Augmentation (EBA), which adjusts the node degrees in the graph as the augmentation. We then propose a new approach, named Contrastive Link Prediction with Edge Balancing Augmentation (CoEBA), that integrates the proposed EBA and the proposed new contrastive losses to improve the model performance. We conduct experiments on 8 benchmark datasets. The results demonstrate that our proposed CoEBA significantly outperforms the other state-of-the-art link prediction models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
