WalkGAN: Network Representation Learning With Sequence-Based Generative Adversarial Networks-Reference-Cited by-同舟云学术

WalkGAN: Network Representation Learning With Sequence-Based Generative Adversarial Networks

Author:

Jin Taisong¹^ORCID,Yang Xixi²^ORCID,Yu Zhengtao³^ORCID,Luo Han⁴,Zhang Yongmei⁵,Jie Feiran⁶,Zeng Xiangxiang²^ORCID,Jiang Min⁷^ORCID

Affiliation:

1. Department of Computer Science and Technology, School of Informatics, Media Analytics and Computing Laboratory, Xiamen University, Xiamen, China

2. College of Information Science and Engineering, Hunan University, Changsha, China

3. Yunnan Key Laboratory of Artificial Intelligence, Kunming, China

4. Department of Computer Science and Technology, School of Informatics, Xiamen University, Xiamen, China

5. School of Information Science and Technology, North China University of Technology, Beijing, China

6. Science and Technology on Electrooptic Control Laboratory, Luoyang, China

7. School of Informatics, Xiamen University, Xiamen, China

Funder

National Key Research and Development Program of China

National Nature Science Foundation of China

Yunnan Provincial Major Science and Technology Special Plan Projects of China

Hunan Provincial Natural Science Foundation of China

Natural Science Foundation of Fujian Province of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Software

Link

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2. Multi-view learning-based heterogeneous network representation learning;Journal of King Saud University - Computer and Information Sciences;2023-12