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

Jingjia Huang, Zhangheng Li, Nannan Li, Shan Liu, Ge Li; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 6480-6489

Abstract


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. It is able to select nodes that are significant for graph representation adaptively, and generate hierarchical features via aggregating the attention-weighted information in nodes. Additionally, we devise a hierarchical prediction architecture to sufficiently leverage the hierarchical representation and facilitate the model learning. The AttPool module together with the entire training structure can be integrated into existing GCNs, and is trained in an end-to-end fashion conveniently. The experimental results on several graph-classification benchmark datasets with various scales demonstrate the effectiveness of our method.

Related Material


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[bibtex]
@InProceedings{Huang_2019_ICCV,
author = {Huang, Jingjia and Li, Zhangheng and Li, Nannan and Liu, Shan and Li, Ge},
title = {AttPool: Towards Hierarchical Feature Representation in Graph Convolutional Networks via Attention Mechanism},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}