Towards Powerful Graph Contrastive Learning without Negative Examples-Reference-Cited by-同舟云学术

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Towards Powerful Graph Contrastive Learning without Negative Examples

Published:2022-07-18 Issue: Volume: Page:
ISSN:
Container-title:2022 International Joint Conference on Neural Networks (IJCNN)
language:
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Author:

Cen Keting¹,Shen Huawei¹,Cao Qi¹,Xu Bingbing¹,Cheng Xueqi²

Affiliation:

1. Data Intelligence System Research Center, Institute of Computing Technology, Chinese Academy of Sciences,Beijing,China,100190

2. Institute of Computing Technology, Chinese Academy of Sciences,CAS Key Laboratory of Network Data Science and Technology,Beijing,China,100190

Funder

National Natural Science Foundation of China

National Key R&D Program of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9891857/9889787/09892636.pdf?arnumber=9892636

Reference50 articles.

1. node2vec

2. Self-supervised Learning on Graphs: Contrastive, Generative,or Predictive

3. SDGNN: Learning Node Representation for Signed Directed Networks

4. Graph Convolutional Networks using Heat Kernel for Semi-supervised Learning

5. Inductive representation learning on large graphs;hamilton;NeurIPS 2017,0

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1. Towards Alignment-Uniformity Aware Representation in Graph Contrastive Learning;Proceedings of the 17th ACM International Conference on Web Search and Data Mining;2024-03-04

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