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

Tsinghua Science and Technology 2024年第2期29卷 605-616页

作者： Kainan Zhang dongmyung shin Daehee Seo Zhipeng Cai Department of Computer Science Georgia State UniversityAtlantaGA 30303USA LSWare Inc. Seoul 08504Republic of Korea College of Intelligence Information Engineering Sangmyung UniversitySeoul 03016Republic of Korea

Open-source licenses can promote the development of machine learning by allowing others to access,modify,and redistribute the training ***,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 ***,some data may be subject to legal or regulatory restrictions that limit its sharing,regardless of the licensing model ***,obtaining large amounts of labeled data can be difficult,time-consuming,or expensive in many real-world ***-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 ***,the current graph neural network methods lack full usage of graph structures on molecular graphs and social network *** structural features are known to correlate with molecular properties in chemistry,structure information tends to be ignored with sufficient property information ***,the common binary classification task of chemical compounds is unsuitable in the few-shot setting requiring novel ***,this paper focuses on the graph classification tasks of a social network,whose complex topology has an uncertain relationship with its nodes'*** 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 *** experiments demonstrate the competitive performance of our framework respective to other state-of-the-art methods.

关键词： few-shot learning contrastive learning data copyright protection

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