HGN2T: A Simple but Plug-and-Play Framework Extending HGNNs on Heterogeneous Temporal Graphs-Reference-Cited by-同舟云学术

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HGN2T: A Simple but Plug-and-Play Framework Extending HGNNs on Heterogeneous Temporal Graphs

Published:2024-10 Issue:5 Volume:10 Page:620-632
ISSN:2332-7790
Container-title:IEEE Transactions on Big Data
language:
Short-container-title:IEEE Trans. Big Data

Author:

Liu Huan¹^ORCID,Jiao Pengfei²^ORCID,Guo Xuan³^ORCID,Wu Huaming⁴^ORCID,Gao Mengzhou²^ORCID,Zhang Jilin²^ORCID

Affiliation:

1. School of Computer, Hangzhou Dianzi University, Hangzhou, China

2. School of Cyberspace, Hangzhou Dianzi University, Hangzhou, China

3. College of Intelligence and Computing, Tianjin University, Tianjin, China

4. Center for Applied Mathematics, Tianjin University, Tianjin, China

Funder

National Natural Science Foundation of China

Provincial Universities of Zhejiang

Zhejiang Laboratory Open Research Project

Hangzhou Artificial Intelligence Major Scientific and Technological Innovation Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6687317/10663875/10436338.pdf?arnumber=10436338

Reference54 articles.

1. Graph Neural Networks

2. A Comprehensive Survey on Graph Neural Networks

3. A Survey on Role-Oriented Network Embedding

4. Representation learning on graphs: Methods and applications;Hamilton;IEEE Data Eng. Bull.,2017

5. Improving Social Network Embedding via New Second-Order Continuous Graph Neural Networks

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