AttPool: Towards Hierarchical Feature Representation in Graph Convolutional Networks via Attention Mechanism

ICCV 2019 Jingjia Huang Zhangheng Li Nannan Li Shan Liu Ge Li

Graph convolutional networks (GCNs) are potentially short of the ability to learn hierarchical representation for graph embedding, which holds them back in the graph classification task. Here, we propose AttPool, which is a novel graph pooling module based on attention mechanism, to remedy the problem... (read more)

PDF Abstract

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.