LAUSR

Graph Convolutional Network (GCN) is a tool for feature extraction, learning, and inference on graph data, widely applied in numerous scenarios. Despite the great success of GCN, it performs weakly under some application conditions, such as a multiple layers model or severely limited labeled nodes. In this paper, we propose a structural reinforcement-based graph convolutional network (SRGCN), which contains two essential techniques: structural reinforcement and semantic alignment. The model's core is to learn and reinforce structural information for nodes from both feature and graph perspectives and then expect the extracted semantic mappings to be similar by semantic alignment technique. The main advantage of SRGCN is structural reinforcement, i.e. the model improves the propagation of features in the graph by reinforcing the structural semantics. This method will alleviate the problems of over-smoothing and over-fitting. We evaluate the model on three standard datasets with node clustering tasks, and the experimental results demonstrate that SRGCN can outperform relative state-of-the-art (SOTA) baselines.