Graph convolutional networks with higher-order pooling for semisupervised node classification

The information propagation mechanism in graph-structured networks such as social networks is the foundation of network security. The graph convolutional network (GCN) is a powerful approach for semisupervised node classification on graph-structure data. The vertex features which pass through the graph network are affected by the k-hop neighborhood vertices. However, current high-order GCN approaches merged the k-hop neighborhood using coarse pooling and complicated weight parameters. To reduce the computational complexity and preserve topological of the graph data, with weight sharing mechanism we propose a novel GCN based on a novel higher-order pooling layer for semisupervised classification. The proposed model and its variants are experimental studied on several large-scale citation network datasets using semisupervised learning. The experimental results show that the proposed model and its variants have lower computational complexity and achieve the state-of-the-art in the node classification accuracy.