  Graph Neural Networks (GNNs) have achieved promising performance in
semi-supervised node classification in recent years. However, the problem of
insufficient supervision, together with representation collapse, largely limits
the performance of the GNNs in this field. To alleviate the collapse of node
representations in semi-supervised scenario, we propose a novel graph
contrastive learning method, termed Mixed Graph Contrastive Network (MGCN). In
our method, we improve the discriminative capability of the latent feature by
enlarging the margin of decision boundaries and improving the cross-view
consistency of the latent representation. Specifically, we first adopt an
interpolation-based strategy to conduct data augmentation in the latent space
and then force the prediction model to change linearly between samples. Second,
we enable the learned network to tell apart samples across two
interpolation-perturbed views through forcing the correlation matrix across
views to approximate an identity matrix. By combining the two settings, we
extract rich supervision information from both the abundant unlabeled nodes and
the rare yet valuable labeled nodes for discriminative representation learning.
Extensive experimental results on six datasets demonstrate the effectiveness
and the generality of MGCN compared to the existing state-of-the-art methods.

Machine Learning