Debiased Graph Contrastive Learning
  Contrastive learning (CL) has emerged as a dominant technique for
unsupervised representation learning which embeds augmented versions of the
anchor close to each other (positive samples) and pushes the embeddings of
other samples (negative samples) apart. As revealed in recent works, CL can
benefit from hard negative samples (negative samples that are difficult to
distinguish from the anchor). However, we observe minor improvement or even
performance drop when we adopt existing hard negative mining techniques in
Graph Contrastive Learning (GCL). We find that many hard negative samples
similar to anchor point are false negative ones (samples from the same class as
anchor point) in GCL, which is different from CL in computer vision and will
lead to unsatisfactory performance of existing hard negative mining techniques
in GCL. To eliminate this bias, we propose Debiased Graph Contrastive Learning
(DGCL), a novel and effective method to estimate the probability whether each
negative sample is true or not. With this probability, we devise two schemes
(i.e., DGCL-weight and DGCL-mix) to boost the performance of GCL. Empirically,
DGCL outperforms or matches previous unsupervised state-of-the-art results on
several benchmarks and even exceeds the performance of supervised ones.
