A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning-Reference-Cited by-同舟云学术

A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning

Published:2022-10-17 Issue: Volume: Page:
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Container-title:Proceedings of the 31st ACM International Conference on Information & Knowledge Management
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Author:

Sun Li¹,Ye Junda²,Peng Hao³,Yu Philip S.⁴

Affiliation:

1. North China Electric Power University, Beijing, China

2. Beijing University of Posts and Telecommunications, Beijing, China

3. Beihang University, Beijing, China

4. University of Illinois at Chicago, Chicago, IL, USA

Funder

S&T Program of Hebei

National Key R&D Program of China

NSF

the Fundamental Research Funds for the Central Universities

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3511808.3557222

Reference55 articles.

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4. Ines Chami Zhitao Ying Christopher Ré and Jure Leskovec. 2019. Hyperbolic graph convolutional neural networks. In Advances in NeurIPS. 4869--4880. Ines Chami Zhitao Ying Christopher Ré and Jure Leskovec. 2019. Hyperbolic graph convolutional neural networks. In Advances in NeurIPS. 4869--4880.

5. A Simple Framework for Contrastive Learning of Visual Representations;Chen Ting;Proceedings of ICML,2020

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