  While generating better negative samples for contrastive learning has been
widely studied in the areas of CV and NLP, very few work has focused on
graph-structured data. Recently, Mixup has been introduced to synthesize hard
negative samples in graph contrastive learning (GCL). However, due to the
unsupervised learning nature of GCL, without the help of soft labels, directly
mixing representations of samples could inadvertently lead to the information
loss of the original hard negative and further adversely affect the quality of
the newly generated harder negative. To address the problem, in this paper, we
propose a novel method DropMix to synthesize harder negative samples, which
consists of two main steps. Specifically, we first select some hard negative
samples by measuring their hardness from both local and global views in the
graph simultaneously. After that, we mix hard negatives only on partial
representation dimensions to generate harder ones and decrease the information
loss caused by Mixup. We conduct extensive experiments to verify the
effectiveness of DropMix on six benchmark datasets. Our results show that our
method can lead to better GCL performance. Our data and codes are publicly
available at https://github.com/Mayueq/DropMix-Code.
