DAGCN: dual attention graph convolutional networks

Fengwen Chen, Shirui Pan, Jing Jiang, Huan Huo, Guodong Long

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

1 Citation (Scopus)

Abstract

Graph convolutional networks (GCNs) have recently become one of the most powerful tools for graph analytics tasks in numerous applications, ranging from social networks and natural language processing to bioinformatics and chemoinformatics, thanks to their ability to capture the complex relationships between concepts. At present, the vast majority of GCNs use a neighborhood aggregation framework to learn a continuous and compact vector, then performing a pooling operation to generalize graph embedding for the classification task. These approaches have two disadvantages in the graph classification task: (1)when only the largest sub-graph structure (k-hop neighbor) is used for neighborhood aggregation, a large amount of early-stage information is lost during the graph convolution step; (2) simple average/sum pooling or max pooling utilized, which loses the characteristics of each node and the topology between nodes. In this paper, we propose a novel framework called, dual attention graph convolutional networks (DAGCN) to address these problems. DAGCN automatically learns the importance of neighbors at different hops using a novel attention graph convolution layer, and then employs a second attention component, a self-attention pooling layer, to generalize the graph representation from the various aspects of a matrix graph embedding. The dual attention network is trained in an end-to-end manner for the graph classification task. We compare our model with state-of-the-art graph kernels and other deep learning methods. The experimental results show that our framework not only outperforms other baselines but also achieves a better rate of convergence.

Original languageEnglish
Title of host publicationInternational Joint Conference on Neural Networks (IJCNN) 2019
EditorsPlamen Angelov, Manuel Roveri
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781728119854
ISBN (Print)9781728119861
DOIs
Publication statusPublished - 2019
EventIEEE International Joint Conference on Neural Networks 2019 - Budapest, Hungary
Duration: 14 Jul 201919 Jul 2019
https://www.ijcnn.org/

Conference

ConferenceIEEE International Joint Conference on Neural Networks 2019
Abbreviated titleIJCNN 2019
CountryHungary
CityBudapest
Period14/07/1919/07/19
Internet address

Cite this

Chen, F., Pan, S., Jiang, J., Huo, H., & Long, G. (2019). DAGCN: dual attention graph convolutional networks. In P. Angelov, & M. Roveri (Eds.), International Joint Conference on Neural Networks (IJCNN) 2019 [8851698] IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IJCNN.2019.8851698