Robust graph mutual-assistance convolutional networks for semi-supervised node classification tasks

Graph convolutional networks (GCNs) have made substantial progress in semi-supervised learning. However, GCNs are sensitive to graph noise, making the reconstruction of imperfect graph structures essential for adapting GCNs to complex scenarios. Prevalent GCNs employ graph augmentation to generate reliable augmented graphs, thereby mining potential graph information. Since traditional GCN layers can only perform message passing independently within each graph, GCNs primarily concentrate on fusing information from the node embeddings independently learned by the reliable augmented graphs. Nevertheless, the neglect of cross-graph information exchange during message passing limits the model's inter-graph interactions ability. In this paper, we propose a GCN framework called R obust graph m utual-assistance convolutional networks (RamNet), which introduces cross-graph information exchange mechanisms into the GCN layers. First, RamNet employs a multi-metric topology augmentation strategy to generate a synthesized graph, thereby exploring more reliable information. Second, it learns inter-graph interaction information by achieving information exchange between the raw graph and the synthesized graph during message passing. Finally, in addition to utilizing a small amount of supervised information, it also captures rich self-supervision information by maximizing consistency relationship between input graphs. We conduct comprehensive experiments in both standard and noisy scenarios, highlighting the superior performance of RamNet.