In recent years, contrastive learning has emerged as a dominant
self-supervised paradigm, attracting numerous research interests in the field
of graph learning. Graph contrastive learning (GCL) aims to embed augmented
anchor samples close to each other while pushing the embeddings of other
samples (negative samples) apart. However, existing GCL methods require large
and diverse negative samples to ensure the quality of embeddings, and recent
studies typically leverage samples excluding the anchor and positive samples as
negative samples, potentially introducing false negative samples (negatives
that share the same class as the anchor). Additionally, this practice can
result in heavy computational burden and high time complexity of $O(N^2)$,
which is particularly unaffordable for large graphs. To address these
deficiencies, we leverage rank learning and propose a simple yet effective
model, GraphRank. Specifically, we first generate two graph views through
corruption. Then, we compute the similarity of pairwise nodes (anchor node and
positive node) in both views, an arbitrary node in the latter view is selected
as a negative node, and its similarity with the anchor node is computed. Based
on this, we introduce rank-based learning to measure similarity scores which
successfully relieve the false negative provlem and decreases the time
complexity from $O(N^2)$ to $O(N)$. Moreover, we conducted extensive
experiments across multiple graph tasks, demonstrating that GraphRank performs
favorably against other cutting-edge GCL methods in various tasks.

对比学习在图学习领域吸引了大量研究兴趣，现有的图对比学习方法需要大规模和多样化的负样本来确保嵌入的质量，但这样会引入虚假的负样本，同时增加了计算负担和时间复杂度，为解决这些问题，提出了一种简单而有效的模型 GraphRank，通过引入基于排名的学习来测量相似度得分，成功地缓解了虚假负样本问题，并将时间复杂度从 O (N^2) 降低到 O (N)，并在多个图任务上展开了广泛实验，证明 GraphRank 在各种任务中表现优异。