  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.
