Semi-Supervised Node Classification With Discriminable Squeeze Excitation Graph Convolutional Networks

Abstract

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.

Keywords

• Aggregation
• capsule network
• graph convolutional networks
• squeeze and excitation