Refining Euclidean Obfuscatory Nodes Helps: A Joint-Space Graph Learning Method for Graph Neural Networks-Reference-Cited by-同舟云学术

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Refining Euclidean Obfuscatory Nodes Helps: A Joint-Space Graph Learning Method for Graph Neural Networks

Published:2024-09 Issue:9 Volume:35 Page:11720-11733
ISSN:2162-237X
Container-title:IEEE Transactions on Neural Networks and Learning Systems
language:
Short-container-title:IEEE Trans. Neural Netw. Learning Syst.

Author:

Liu Zhaogeng¹^ORCID,Ji Feng²^ORCID,Yang Jielong¹^ORCID,Cao Xiaofeng¹^ORCID,Zhang Muhan³,Chen Hechang¹^ORCID,Chang Yi⁴^ORCID

Affiliation:

1. School of Artificial Intelligence, Jilin University, Changchun, China

2. School of Electrical and Electronic Engineering, Nanyang Technological University, Jurong West, Singapore

3. Institute for Artificial Intelligence, Peking University, Beijing, China

4. Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, Ministry of Education, Changchun, China

Funder

National Key Research and Development Program of China

Young Elite Scientists Sponsorship Program through CAST

Young Scientists Fund of the National Natural Science Foundation of China

National Natural Science Foundation of China

Key R&D Project of Jilin Province

International Cooperation Project of Jilin Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/5962385/10663876/10557658.pdf?arnumber=10557658

Reference67 articles.

1. Geometric Deep Learning: Going beyond Euclidean data

2. Why Do Attributes Propagate in Graph Convolutional Neural Networks?

3. Heterogeneous Graph Neural Networks for Extractive Document Summarization

4. Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks

5. Multi-Modal Graph Neural Network for Joint Reasoning on Vision and Scene Text

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