OMG: Towards Effective Graph Classification Against Label Noise-Reference-Cited by-同舟云学术

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OMG: Towards Effective Graph Classification Against Label Noise

Published:2023-12-01 Issue:12 Volume:35 Page:12873-12886
ISSN:1041-4347
Container-title:IEEE Transactions on Knowledge and Data Engineering
language:
Short-container-title:IEEE Trans. Knowl. Data Eng.

Author:

Yin Nan¹^ORCID,Shen Li²^ORCID,Wang Mengzhu³^ORCID,Luo Xiao⁴^ORCID,Luo Zhigang³^ORCID,Tao Dacheng⁵^ORCID

Affiliation:

1. Mohamed Bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE

2. JD Explore Academy, Beijing, China

3. National University of Defense Technology, Changsha, Hunan, China

4. University of California, Los Angeles, CA, USA

5. University of Sydney, Camperdown, NSW, Australia

Funder

Science and Technology Innovation 2030

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/69/10311056/10113198.pdf?arnumber=10113198

Reference77 articles.

1. Weisfeiler-Lehman graph kernels;shervashidze;J Mach Learn Res,2011

2. Bootstrap your own latent-a new approach to self-supervised learning;grill;Proc Int Conf Learn Representations,2021

3. Shortest-Path Kernels on Graphs

4. Expectation pooling: an effective and interpretable pooling method for predicting DNA–protein binding

5. Understanding deep learning (still) requires rethinking generalization

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. HMCDA: a novel method based on the heterogeneous graph neural network and metapath for circRNA-disease associations prediction;BMC Bioinformatics;2023-09-11

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