Explanation-based Graph Neural Networks for Graph Classification

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Graph Neural Network models can be used to quickly analyze interactions between multiple data expressed in a graph structure, with high accuracy. Previous studies accurately extract subgraphs which have a significant influence on the whole graph, providing accurate explanations for predictions of GNN. We noted that explanation components could help improve classification performance as unique representations of each class. Therefore, we suggest the GNN performance can be further improved by using explanation components. In this paper, we propose an Explanation-Based Graph Neural Networks (EBGNN) that utilizes contrastive learning at the instance level, by applying explanation components. In EBGNN, the explanation components ensure similarity for instances within the same class, and promote separability for instances in different classes. Finally, we conducted an evaluation on five benchmark datasets (MUTAG, IMDB-BINARY, PROTEINS, NCI1, and DD). Our experiment showed a significant increase in graph classification performance compared to state-of-the-art methods.
Publisher
IEEE
Issue Date
2022-08-24
Language
English
Citation

International Conference on Pattern Recognition, ICPR 2022, pp.2836 - 2842

ISSN
1051-4651
DOI
10.1109/ICPR56361.2022.9956478
URI
http://hdl.handle.net/10203/300942
Appears in Collection
EE-Conference Papers(학술회의논문)
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