In recent years, Graph Convolutional Networks (GCNs) have been increasingly and widely used in graph data representation and semi-supervised learning. GCNs can reveal and dig deep into irregular data with spatial topological structure. However, in the task of node classification, most models will be over-smoothing (indistinguishable representations of nodes in different classes) after stacking multilayer GCNs. To alleviate the issue of over-smoothing, we propose a novel Discriminable Squeeze and Excitation Graph Convolutional Network (D-SEGCN) based on the attention mechanism of features for semi-supervised node classification. In the proposed D-SEGCN model, Squeeze and Excitation (SE) module is fused to the Graph Convolutional Networks to form SEGCN, which can selectively enhance significant features and realize the adaptive calibration of feature dimensions, thus enhancing node features. Then, the feature representation is obtained through a hierarchical structure, it is put into a discriminable capsule layer which can judge the similarities between the node features to obtain new feature. Finally, the feature representation and new feature are fused to obtain the final feature to strengthen the discriminability of nodes. Furthermore, we demonstrate that D-SEGCN significantly outperforms the state-of-the-art methods on three citation networks datasets.

Graph convolutional networks (GCNs) are neural network frameworks for machine learning on graphs. They can simultaneously perform end-to-end learning on the attribute information and the structure information of graph data. However, most existing GCNs inevitably encounter the limitations of non-robustness and low classification accuracy when labeled nodes are scarce. To address the two issues, the deep graph convolutional generative adversarial network (DGCGAN), a model combining GCN and deep convolutional generative adversarial networks (DCGAN), is proposed in this paper. First, the graph data is mapped to a highly nonlinear space by using the topology and attribute information of the graph for symmetric normalized Laplacian transform. Then, through the feature-structured enhanced module, the node features are expanded into regular structured data, such as images and sequences, which are input to DGCGAN as positive samples, thus expanding the sample capacity. In addition, the feature-enhanced (FE) module is adopted to enhance the typicality and discriminability of node features, and to obtain richer and more representative features, which is helpful for facilitating accurate classification. Finally, additional constraints are added to the network model by introducing DCGAN, thus enhancing the robustness of the model. Through extensive empirical studies on several standard benchmarks, we find that DGCGAN outperforms state-of-the-art baselines on semi-supervised node classification and remote sensing image classification.