Sparse‐Dyn: Sparse dynamic graph multirepresentation learning via event‐based sparse temporal attention network-Reference-Cited by-同舟云学术

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Sparse‐Dyn: Sparse dynamic graph multirepresentation learning via event‐based sparse temporal attention network

Published:2022-07-25 Issue:11 Volume:37 Page:8770-8789
ISSN:0884-8173
Container-title:International Journal of Intelligent Systems
language:en
Short-container-title:Int J of Intelligent Sys

Author:

Pang Yan¹^ORCID,Shan Ai¹,Wang Zhen²^ORCID,Wang Mengyu³^ORCID,Li Jianwei⁴,Zhang Ji⁵^ORCID,Huang Teng¹,Liu Chao⁶^ORCID

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 Information Science and Technology Osaka University Osaka Japan

4. Department of Computer Science San Jose State University San Jose 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.22967

Reference52 articles.

1. PerozziB Al‐RfouR SkienaS.Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining;2014:701‐710.

2. Faces are Protected as Privacy: An Automatic Tagging Framework Against Unpermitted Photo Sharing in Social Media

3. The Dynamic Privacy-preserving Mechanisms for Online Dynamic Social Networks

4. GroverA LeskovecJ.node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining;2016:855‐864.

5. WangZ ZhangJ FengJ ChenZ.Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the AAAI Conference on Artificial Intelligence. Vol 28;2014.

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