Line graph contrastive learning for link prediction

Zehua Zhang, Shilin Sun, Guixiang Ma, Caiming Zhong

Abstract: Highlights•We design a novel contrastive learning framework based on line graph to be suitable for link prediction on sparse and dense graphs.•We propose a cross-scale contrastive learning strategy to maximize the mutual information between subgraph and line graph.•The dual perspectives contrastive progress to some extent avoids the problem of inconsistent prediction on the similarity based methods with a single view.•Our comprehensive experiments on six datasets from diverse areas demonstrate that our model has better performance on generalization and robustness than the SOTA methods.