  Graph representation learning (GRL) has emerged as a powerful technique for
solving graph analytics tasks. It can effectively convert discrete graph data
into a low-dimensional space where the graph structural information and graph
properties are maximumly preserved. While there is rich literature on node and
whole-graph representation learning, GRL for link is relatively less studied
and less understood. One common practice in previous works is to generate link
representations by directly aggregating the representations of their incident
nodes, which is not capable of capturing effective link features. Moreover,
common GRL methods usually rely on full-graph training, suffering from poor
scalability and high resource consumption on large-scale graphs. In this paper,
we design Subgraph Contrastive Link Representation Learning (SCLRL) -- a
self-supervised link embedding framework, which utilizes the strong correlation
between central links and their neighborhood subgraphs to characterize links.
We extract the "link-centric induced subgraphs" as input, with a subgraph-level
contrastive discrimination as pretext task, to learn the intrinsic and
structural link features via subgraph mini-batch training. Extensive
experiments conducted on five datasets demonstrate that SCLRL has significant
performance advantages in link representation learning on benchmark datasets
and prominent efficiency advantages in terms of training speed and memory
consumption on large-scale graphs, when compared with existing link
representation learning methods.
