Multi-Channel Equilibrium Graph Neural Network for Multi-View Semi-Supervised Learning

In practical applications, the difficulty of multi-view data annotation poses a challenge for multi-view semi-supervised learning. Although some graph-based approaches have been proposed for this task, they often struggle with capturing long-range information and memory bottlenecks, and usually encounter over-smoothing. To address these issues, this paper proposes an implicit model, named multi-channel Equilibrium Graph Neural Network (MEGNN). Through an equilibrium point iterative process, the proposed MEGNN naturally captures long-range information and effectively reduces the consumption of memory compared with explicit models. Furthermore, the proposed method deals with the issue of over-smoothing in deep graph convolutional networks by residual connection and shrinkage factor. We analyze the effect of the shrinkage factor on the information capturing capability of the model, and demonstrate that the proposed method does not encounter over-smoothing. Comprehensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods.