Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning-Reference-Cited by-同舟云学术

Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning

Published:2021 Issue: Volume: Page:1-15
ISSN:2162-237X
Container-title:IEEE Transactions on Neural Networks and Learning Systems
language:
Short-container-title:IEEE Trans. Neural Netw. Learning Syst.

Author:

Liu Yixin¹,Li Zhao²^ORCID,Pan Shirui³^ORCID,Gong Chen⁴^ORCID,Zhou Chuan⁵^ORCID,Karypis George⁶

Affiliation:

1. Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton, VIC 3800, Australia.

2. Alibaba Group, Hangzhou 310000, China.

3. Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton, VIC 3800, Australia (e-mail: shirui.pan@monash.edu)

4. PCA Laboratory, Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China, and also with the Department of Computing, The Hong Kong Polytechnic University, Hong Kong.

5. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100093, China.

6. Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455 USA.

Funder

National Natural Science Foundation NSF of China

Fundamental Research Funds for the Central Universities

China Computer Federation CCF-Tencent Open Fund

Hong Kong Scholars Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

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

Link

http://xplorestaging.ieee.org/ielx7/5962385/6104215/09395172.pdf?arnumber=9395172

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