Graph transformer-convolution network for graph classification

Chengzong Li; Rui Zhai; Fang Zuo; Libo Zhang; Junyang Yu

doi:10.1117/12.2624865

6 December 2021 Graph transformer-convolution network for graph classification

Chengzong Li, Rui Zhai, Fang Zuo, Libo Zhang, Junyang Yu

Proceedings Volume 12085, International Conference on Green Communication, Network, and Internet of Things (GCNIoT 2021); 1208506 (2021) https://doi.org/10.1117/12.2624865
Event: 2021 International Conference on Green Communication, Network, and Internet of Things, 2021, Kunming, China

Abstract

The Transformer has achieved tremendous success in computer vision, natural language processing, and graph representation learning. However, the transformer cannot effectively encode the topology information of the graph into the model, while it is the advantage of the graph convolution network (GCN). Therefore, we propose a model GTGC combining transformer and GCN for graph classification tasks. To this end, we take the result of graph data passing through multi-head self-attention and feed-forward blocks as the input of the graph convolution module. By increasing the number of neighbors for each node’s feature matrix, the nodes with more neighbors are more important in the attention mechanism. We validate the validity of the model on multiple data sets, including social network datasets and bioinformatics datasets. Experimental results demonstrate that our model achieves advanced accuracy

Citation Download Citation

Chengzong Li, Rui Zhai, Fang Zuo, Libo Zhang, and Junyang Yu "Graph transformer-convolution network for graph classification", Proc. SPIE 12085, International Conference on Green Communication, Network, and Internet of Things (GCNIoT 2021), 1208506 (6 December 2021); https://doi.org/10.1117/12.2624865

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KEYWORDS

Transformers

Convolution

Data modeling

Social networks

Bioinformatics

Computer programming

Visual process modeling

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