针对稀疏图链接预测，提出用属性增强的相似度排序方法
Attribute-Enhanced Similarity Ranking for Sparse Link Prediction
- 结合节点属性与拓扑结构，提升相似度计算精度
- 采用排名损失和分块负采样，有效应对类别不平衡问题
- 在真实稀疏场景下优于主流GNN模型，适合低密度图任务
链接预测是图数据中的基础问题。在最现实的设定中，需预测随机节点对之间缺失或未来的连接。图神经网络（GNN）已成为主流框架，通常将链接预测视为二分类问题，并通过均匀采样平衡的断开节点对进行训练和评估。然而我们发现，现有GNN在平衡设置下的性能无法迁移到更真实的非平衡设置，且简单的拓扑方法在处理稀疏性时表现更优。为此，本文提出Gelato——一种基于相似度的链接预测方法：(1) 利用节点属性进行图学习，以增强拓扑启发式；(2) 使用排名损失缓解类别不平衡；(3) 通过图划分实现高效难例负采样。实验表明，Gelato在多个数据集上优于现有的GNN方法。
原文摘要 · Abstract (English)
Link prediction is a fundamental problem in graph data. In its most realistic setting, the problem consists of predicting missing or future links between random pairs of nodes from the set of disconnected pairs. Graph Neural Networks (GNNs) have become the predominant framework for link prediction. GNN-based methods treat link prediction as a binary classification problem and handle the extreme class imbalance -- real graphs are very sparse -- by sampling (uniformly at random) a balanced number of disconnected pairs not only for training but also for evaluation. However, we show that the reported performance of GNNs for link prediction in the balanced setting does not translate to the more realistic imbalanced setting and that simpler topology-based approaches are often better at handling sparsity. These findings motivate Gelato, a similarity-based link-prediction method that applies (1) graph learning based on node attributes to enhance a topological heuristic, (2) a ranking loss for addressing class imbalance, and (3) a negative sampling scheme that efficiently selects hard training pairs via graph partitioning. Experiments show that Gelato outperforms existing GNN-based alternatives.
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