Semantic-enhanced graph neural networks with global context representation

Node classification is a crucial task for efficiently analyzing graph-structured data. Related semi-supervised methods have been extensively studied to address the scarcity of labeled data in emerging classes. However, two fundamental weaknesses hinder the performance: lacking the ability to mine latent semantic information between nodes, or ignoring to simultaneously capture local and global coupling dependencies between different nodes. To solve these limitations, we propose a novel semantic-enhanced graph neural networks with global context representation for semi-supervised node classification. Specifically, we first use graph convolution network to learn short-range local dependencies, which not only considers the spatial topological structure relationship between nodes, but also takes into account the semantic correlation between nodes to enhance the representation ability of nodes. Second, an improved Transformer model is introduced to reasoning the long-range global pairwise relationships, which has linear computational complexity and is particularly important for large datasets. Finally, the proposed model shows strong performance on various open datasets, demonstrating the superiority of our solutions.