🤖 AI Summary
To address label sparsity and representation collapse in semi-supervised node classification, this paper proposes the Mixed Graph Contrastive Network (MGCN). MGCN jointly models discriminative structural information for both labeled and unlabeled nodes through latent-space interpolation augmentation and cross-view correlation constraints. Specifically, it introduces an interpolation-driven linear prediction consistency constraint to explicitly integrate limited supervised signals with self-supervised signals; concurrently, it enforces identity approximation of the cross-view correlation matrix to suppress representation redundancy and mitigate collapse. Evaluated on six benchmark datasets, MGCN consistently outperforms existing state-of-the-art methods. The implementation is publicly available, demonstrating strong effectiveness, robustness to varying label rates and graph perturbations, and generalization across diverse graph topologies and feature distributions.
📝 Abstract
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 embeddings by an interpolation-based augmentation strategy and a correlation reduction mechanism. Specifically, we first conduct the interpolation-based 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. The code of MGCN is available at https://github.com/xihongyang1999/MGCN on Github.
