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:链接的预测任务侧重于预测未来可能的联系。 大部分现有研究都测量了结节配对不同相似分数连接的可能性并预测了节点之间的联系。 但是,基于相似性的方法在节点的信息丢失和相似指数的概括性能力方面存在一些挑战。 为了解决上述问题,我们建议使用线形图表对比学习(LGCL)方法,从多个角度获取丰富的信息。 LGCL 获得Hhh-h 带目标节点的子图取样的子图视图。 在将抽样子图转换成直线图后,链接的预测任务被转换为节点分类任务, 以图变进图能够更有效地从图表中学习边缘嵌入。 然后我们在线形图和子图上设计新的跨尺度对比学习框架, 以最大限度地增加相互的信息, 从而将结构和特征信息结合起来。 实验结果显示, 拟议的 LGCL 显示, 节点比最先进的方法快化, 并在一般化和稳健性方面有更好的表现。</s>