Physics-Informed Explainable Continual Learning on Graphs-Reference-Cited by-同舟云学术

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Physics-Informed Explainable Continual Learning on Graphs

Published:2024-09 Issue:9 Volume:35 Page:11761-11772
ISSN:2162-237X
Container-title:IEEE Transactions on Neural Networks and Learning Systems
language:
Short-container-title:IEEE Trans. Neural Netw. Learning Syst.

Author:

Peng Ciyuan¹^ORCID,Tang Tao¹^ORCID,Yin Qiuyang²^ORCID,Bai Xiaomei³^ORCID,Lim Suryani¹,Aggarwal Charu C.⁴^ORCID

Affiliation:

1. Institute of Innovation, Science and Sustainability, Federation University Australia, Ballarat, VIC, Australia

2. School of Engineering, Guangzhou College of Technology and Business, Foshan, China

3. School of Artificial Intelligence, Anshan Normal University, Anshan, China

4. IBM T. J. Watson Research Center, Yorktown Heights, NY, USA

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/5962385/10663876/10387470.pdf?arnumber=10387470

Reference68 articles.

1. Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning

2. T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction

3. Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction

4. On temporal graph exploration

5. Computing maximum matchings in temporal graphs

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