Self-Supervised Teaching and Learning of Representations on Graphs-Reference-Cited by-同舟云学术

Self-Supervised Teaching and Learning of Representations on Graphs

Published:2023-04-30 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the ACM Web Conference 2023
language:
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Author:

Wan Liangtian¹^ORCID,Fu Zhenqiang¹^ORCID,Sun Lu²^ORCID,Wang Xianpeng³^ORCID,Xu Gang⁴^ORCID,Yan Xiaoran⁵^ORCID,Xia Feng⁶^ORCID

Affiliation:

1. Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, School of Software, Dalian University of Technology, China

2. Department of Communication Engineering, Institute of Information Science Technology, Dalian Maritime University, China

3. State Key Laboratory of Marine Resource Utilization in South China Sea, School of Information and Communication Engineering, Hainan University, China

4. State Key Laboratory of Millimeter Waves, School of Information Science and Engineering, Southeast University, China

5. Research Center of Big Data Intelligence, Research Institute of Artificial Intelligence, Zhejiang Lab, China

6. School of Computing Technologies, RMIT University, Australia

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Natural Science Foundation of Liaoning Province

Fundamental Research Funds for the Central Universities

Zhejiang Lab

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3543507.3583441

Reference57 articles.

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2. Jie Chen , Tengfei Ma , and Cao Xiao . 2018 . FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling . In International Conference on Learning Representations. Jie Chen, Tengfei Ma, and Cao Xiao. 2018. FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling. In International Conference on Learning Representations.

3. Ting Chen , Simon Kornblith , Mohammad Norouzi , and Geoffrey Hinton . 2020 . A simple framework for contrastive learning of visual representations . In International Conference on Machine Learning. PMLR, 1597–1607 . Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International Conference on Machine Learning. PMLR, 1597–1607.

4. Djork-Arné Clevert Thomas Unterthiner and Sepp Hochreiter. 2015. Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289(2015). Djork-Arné Clevert Thomas Unterthiner and Sepp Hochreiter. 2015. Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289(2015).

5. Michaël Defferrard , Xavier Bresson , and Pierre Vandergheynst . 2016. Convolutional neural networks on graphs with fast localized spectral filtering. Advances in Neural Information Processing Systems 29 ( 2016 ). Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. Advances in Neural Information Processing Systems 29 (2016).

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