Link prediction tasks focus on predicting possible future connections. Most existing researches measure the likelihood of links by different similarity scores on node pairs and predict links between nodes. However, the similarity-based approaches have some challenges in information loss on nodes and generalization ability on similarity indexes. To address the above issues, we propose a Line Graph Contrastive Learning(LGCL) method to obtain rich information with multiple perspectives. LGCL obtains a subgraph view by h-hop subgraph sampling with target node pairs. After transforming the sampled subgraph into a line graph, the link prediction task is converted into a node classification task, which graph convolution progress can learn edge embeddings from graphs more effectively. Then we design a novel cross-scale contrastive learning framework on the line graph and the subgraph to maximize the mutual information of them, so that fuses the structure and feature information. The experimental results demonstrate that the proposed LGCL outperforms the state-of-the-art methods and has better performance on generalization and robustness.

翻译：链路预测任务旨在预测可能存在的未来连接。现有研究大多通过节点对的不同相似度分数来衡量链路可能性，并预测节点间的连接。然而，基于相似度的方法在节点信息丢失和相似度指标泛化能力方面存在挑战。为解决上述问题，我们提出一种链路图对比学习（LGCL）方法，通过多视角获取丰富信息。LGCL通过目标节点对的h跳子图采样获得子图视图，将采样子图转换为链路图后，将链路预测任务转化为节点分类任务，使图卷积过程能够更有效地从图中学习边嵌入。随后，我们在链路图和子图上设计了一种新颖的跨尺度对比学习框架，通过最大化二者互信息来融合结构与特征信息。实验结果表明，所提出的LGCL方法优于现有最先进方法，并在泛化性和鲁棒性方面展现出更优性能。