Graph Decipher: A transparent dual‐attention graph neural network to understand the message‐passing mechanism for the node classification-Reference-Cited by-同舟云学术

Graph Decipher: A transparent dual‐attention graph neural network to understand the message‐passing mechanism for the node classification

Published:2022-07-25 Issue:11 Volume:37 Page:8747-8769
ISSN:0884-8173
Container-title:International Journal of Intelligent Systems
language:en
Short-container-title:Int J of Intelligent Sys

Author:

Pang Yan¹^ORCID,Huang Teng¹,Wang Zhen²^ORCID,Li Jianwei³^ORCID,Hosseini Poorya⁴^ORCID,Zhang Ji⁵^ORCID,Liu Chao⁶^ORCID,Ai Shan¹

Affiliation:

1. Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou Guangdong China

2. Research Center for Big Data Intelligence Zhejiang Lab Hangzhou Zhejiang China

3. Department of Computer Science San Jose State University San Jose California USA

4. Applied Physics Laboratory Johns Hopkins University El Segundo California USA

5. School of Mathematics, Physics and Computing University of Southern Queensland Toowoomba Queensland Australia

6. Department of Electrical Engineering University of Colorado Denver Denver Colorado USA

Publisher

Hindawi Limited

Subject

Artificial Intelligence,Human-Computer Interaction,Theoretical Computer Science,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/int.22966

Reference59 articles.

1. HamiltonWL YingR LeskovecJ.Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems;2017:1025‐1035.

2. The dynamic privacy‐preserving mechanisms for online dynamic social networks;Li J;IEEE Trans Knowl Data Eng,2020

3. FoutA ByrdJ ShariatB Ben‐HurA.Protein interface prediction using graph convolutional networks. In: Proceedings of the 31st International Conference on Neural Information Processing Systems;2017:6533‐6542.

4. Fostering Neuroethics Integration with Neuroscience in the BRAIN Initiative: Comments on the NIH Neuroethics Roadmap

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