Dynamic depth-width optimization for capsule graph convolutional network  

作　　者：Shangwei WU Yingtong XIONG Chuliang WENG 

机构地区：[1]School of Data Science and Engineering,East China Normal University,Shanghai 200062,China

出　　处：《Frontiers of Computer Science》2023年第6期159-161,共3页中国计算机科学前沿（英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.62141214 and 62272171).

摘　　要：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.

关 键 词：CONVOLUTION REPRESENTATION mutual 

分 类 号：O15[理学—数学]