Dynamic depth-width optimization for capsule graph convolutional network

1 Introduction Encouraged by the success of Convolutional Neural Networks(CNNs),many studies[1],known as Graph Convolutional Networks(GCNs),borrowed the idea of convolution and redefined it for graph data.In graph-level classification tasks,Classic GCN methods[2,3]generate graph embeddings based on the learned node embeddings which consider each node’s representation as multiple independent scalar features.However,they neglect the detailed mutual relations among different node features such as position,direction,and connection.Inspired by CapsNet[4]which encodes each feature of an image as a vector(a capsule),CapsGNN[5]extracts multi-scale node features from different convolutional layers in the form of capsules.However,CapsGNN uses a static model structure to conduct training,which inherently restricts its representation ability on different datasets.