Graph Self-Supervised Learning: A Survey-Reference-Cited by-同舟云学术

Graph Self-Supervised Learning: A Survey

Author:

Liu Yixin¹,Jin Ming¹,Pan Shirui²,Zhou Chuan³,Zheng Yu⁴,Xia Feng⁵,Yu Philip⁶

Affiliation:

1. Department of Data Science and AI, Monash University, 2541 Clayton, Victoria, Australia

2. Faculty of Information Technology, Monash University, 2541 Clayton, Victoria, Australia, 3800

3. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, 12381 Beijing, Beijing, China, 100864

4. Department of Computer Science and Information Technology, La Trobe University, 2080 Melbourne, Victoria, Australia

5. School of Engineering, IT and Physical Sciences, Federation University Australia, 1458 Ballarat, Victoria, Australia, 3353

6. Computer Science, University of Illinois at Chicago, 14681 Chicago, Illinois, United States, 60607-7101

Funder

Australian Research Council

National Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems

Link

Reference162 articles.

1. Self-supervised graph representation learning via global context prediction;peng,2020

2. CAGNN: Cluster-aware graph neural networks for unsupervised graph representation learning;zhu,2020

3. Multilevel graph partitioning schemes;karypis;Proc Int Conf Parallel Process,1995

5. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion;vincent;J Mach Learn Res,2010

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