  Graph Contrastive Learning (GCL) has shown promising performance in graph
representation learning (GRL) without the supervision of manual annotations.
GCL can generate graph-level embeddings by maximizing the Mutual Information
(MI) between different augmented views of the same graph (positive pairs).
However, the GCL is limited by dimensional collapse, i.e., embedding vectors
only occupy a low-dimensional subspace. In this paper, we show that the
smoothing effect of the graph pooling and the implicit regularization of the
graph convolution are two causes of the dimensional collapse in GCL. To
mitigate the above issue, we propose a non-maximum removal graph contrastive
learning approach (nmrGCL), which removes "prominent'' dimensions (i.e.,
contribute most in similarity measurement) for positive pair in the pre-text
task. Comprehensive experiments on various benchmark datasets are conducted to
demonstrate the effectiveness of nmrGCL, and the results show that our model
outperforms the state-of-the-art methods. Source code will be made publicly
available.
