Multi-Channel Fusion Graph Convolutional Networks with pseudo-label for Semi-Supervised Node Classification

Graph Convolutional Networks GCNs have shown great promise in semi-supervised node classification tasks. However, existing Graph Convolutional Networks GCNs face two key challenges: 1 While addressing the limitations of incomplete or noisy graph structures, the structural information of the graph remains underutilized 2 the scarcity of labeled data, limiting the ability to learn comprehensive embeddings. To address these issues, we propose a novel Multi-channel Fusion Graph Convolutional Networks with pseudo-label, which learn a connected embedding by fusing the multi-channel graphs information and node features. First, to explore the latent information within the original data, we design a graph generation module to extend and reconstruct the original data into multiple graphs. Meanwhile, a multi-channel approach is employed to embed and fuse these graphs, capturing the complementarity across different channels. Second, to address the issue of label sparsity, we design a confidence propagation-based information gain filtering module to generate high-quality pseudo-labels. Extensive experiments on three benchmark datasets demonstrate that our method outperforms other approaches.