用自监督学习提升相似度链接预测性能，无需标签也能显著提效。
Can Self Supervision Rejuvenate Similarity-Based Link Prediction?
- 设计双视角对比学习框架，自动挖掘更丰富的节点表示
- 在无标签场景下，链接预测AUC最高提升21.2%相比传统方法
- 适合缺乏标注数据的图分析任务，如社交网络、知识图谱
尽管基于端到端学习的链接预测方法取得了显著进展，但在无标签的无监督场景中，传统的相似度驱动型链接预测方法依然具有重要意义。然而，相似度计算所依赖的节点特征选择往往困难，低信息量特征会导致性能下降。为解决该问题，本文将自监督图学习技术融入相似度驱动的链接预测，提出一种新方法：自监督相似度链接预测（3SLP）。该方法适用于无已知链接标签的无监督环境。具体而言，3SLP引入一种精心设计的数据增强与双视角对比节点表征学习（DCNRL），旨在生成更具信息量的节点表示，替代原始节点属性作为相似度计算的输入。在多个基准数据集上的大量实验表明，3SLP表现优异，相较传统相似度方法最高提升21.2%（AUC）。
原文摘要 · Abstract (English)
Although recent advancements in end-to-end learning-based link prediction (LP) methods have shown remarkable capabilities, the significance of traditional similarity-based LP methods persists in unsupervised scenarios where there are no known link labels. However, the selection of node features for similarity computation in similarity-based LP can be challenging. Less informative node features can result in suboptimal LP performance. To address these challenges, we integrate self-supervised graph learning techniques into similarity-based LP and propose a novel method: Self-Supervised Similarity-based LP (3SLP). 3SLP is suitable for the unsupervised condition of similarity-based LP without the assistance of known link labels. Specifically, 3SLP introduces a dual-view contrastive node representation learning (DCNRL) with crafted data augmentation and node representation learning. DCNRL is dedicated to developing more informative node representations, replacing the node attributes as inputs in the similarity-based LP backbone. Extensive experiments over benchmark datasets demonstrate the salient improvement of 3SLP, outperforming the baseline of traditional similarity-based LP by up to 21.2% (AUC).
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