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Open Access

Few-Shot Graph Classification with Structural-Enhanced Contrastive Learning for Graph Data Copyright Protection

Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA
LSWare Inc., Seoul 08504, Republic of Korea
College of Intelligence Information Engineering, Sangmyung University, Seoul 03016, Republic of Korea
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Abstract

Open-source licenses can promote the development of machine learning by allowing others to access, modify, and redistribute the training dataset. However, not all open-source licenses may be appropriate for data sharing, as some may not provide adequate protections for sensitive or personal information such as social network data. Additionally, some data may be subject to legal or regulatory restrictions that limit its sharing, regardless of the licensing model used. Hence, obtaining large amounts of labeled data can be difficult, time-consuming, or expensive in many real-world scenarios. Few-shot graph classification, as one application of meta-learning in supervised graph learning, aims to classify unseen graph types by only using a small amount of labeled data. However, the current graph neural network methods lack full usage of graph structures on molecular graphs and social network datasets. Since structural features are known to correlate with molecular properties in chemistry, structure information tends to be ignored with sufficient property information provided. Nevertheless, the common binary classification task of chemical compounds is unsuitable in the few-shot setting requiring novel labels. Hence, this paper focuses on the graph classification tasks of a social network, whose complex topology has an uncertain relationship with its nodes’ attributes. With two multi-class graph datasets with large node-attribute dimensions constructed to facilitate the research, we propose a novel learning framework that integrates both meta-learning and contrastive learning to enhance the utilization of graph topological information. Extensive experiments demonstrate the competitive performance of our framework respective to other state-of-the-art methods.

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Tsinghua Science and Technology
Pages 605-616
Cite this article:
Zhang K, Shin D, Seo D, et al. Few-Shot Graph Classification with Structural-Enhanced Contrastive Learning for Graph Data Copyright Protection. Tsinghua Science and Technology, 2024, 29(2): 605-616. https://doi.org/10.26599/TST.2023.9010071

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Received: 09 June 2023
Revised: 12 July 2023
Accepted: 15 July 2023
Published: 22 September 2023
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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