Few-Shot Graph Classification with Structural-Enhanced Contrastive Learning for Graph Data Copyright Protection
Author:
Affiliation:
1. Georgia State University,Department of Computer Science,Atlanta,GA,USA,30303
2. LSWare Inc.,Seoul,Republic of Korea,08504
3. College of Intelligence Information Engineering, Sangmyung University,Seoul,Republic of Korea,03016
Publisher
Tsinghua University Press
Subject
Multidisciplinary
Link
http://xplorestaging.ieee.org/ielx7/5971803/10258149/10258268.pdf?arnumber=10258268
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2. An empirical study of graph contrastive learning;zhu;Arxiv preprint arXiv,2021
3. A fair comparison of graph neural networks for graph classification;errica;Arxiv preprint arXiv,2019
4. Are graph augmentations necessary? Simple graph contrastive learning for recommendation;yu;Proc Int'l ACM SIGIR Conf Research and Development in Information Retrieval,2022
5. Generalizing from a few examples: A survey on few-shot learning;wang;ACM Comput Surv,2020
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