Yixin Liu

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Shirui PanFull Professor, ARC Future Fellow, FQA, Director of TrustAGI Lab, Griffith UniversityVerified email at griffith.edu.au
Ming JinAssistant Professor, School of ICT, Griffith UniversityVerified email at griffith.edu.au
Chuan ZhouAcademy of Mathematics and Systems Science, Chinese Academy of SciencesVerified email at amss.ac.cn
Philip S. YuProfessor of Computer Science, University of Illinons at ChicagoVerified email at cs.uic.edu
Kaize DingAssistant Professor of Stats & Data Science, Northwestern UniversityVerified email at northwestern.edu
VCS Lee, Vincent C.S. Lee, CS Lee, V...Monash UniversityVerified email at monash.edu
Zhao LiAdjunct Professor, Zhejiang University, Yugu Tech, Zhejiang Lab, Alibaba GroupVerified email at zju.edu.cn
Xin ZhengAssistant Professor, School of ICT, Griffith UniversityVerified email at griffith.edu.au
Feng XiaProfessor, School of Computing Technologies, RMIT UniversityVerified email at rmit.edu.au
Daokun ZhangUniversity of Nottingham Ningbo ChinaVerified email at nottingham.edu.cn
Di JinTianjin University, ProfessorVerified email at tju.edu.cn
Yizhen ZhengPhD candidate, Monash UniversityVerified email at monash.edu
Yue TanUniversity of New South WalesVerified email at unsw.edu.au
Guodong LongAssociate Professor, Faculty of Engineering and IT, University of Technology SydneyVerified email at uts.edu.au
Qinghua LuData61, CSIROVerified email at data61.csiro.au
Chen GongProfessorVerified email at sjtu.edu.cn
George KarypisDistinguished McKnight University Professor, University of Minnesota; SPS, AWSVerified email at umn.edu
Chengqi ZhangChair Professor of Artificial IntelligenceVerified email at polyu.edu.hk
Miao ZhangHarbin Institute of Technology (Shenzhen)Verified email at hit.edu.cn
Ming LiProfessor, Zhejiang Normal UniversityVerified email at zjnu.edu.cn

Yixin Liu

Research Fellow, Institute for Integrated and Intelligent Systems (IIIS), Griffith University

Verified email at griffith.edu.au - Homepage

Graph Neural Networks Generative AI Anomaly Detection


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Graph self-supervised learning: A survey Y Liu, M Jin, S Pan, C Zhou, Y Zheng, F Xia, SY Philip IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022	634	2022
Anomaly detection on attributed networks via contrastive self-supervised learning Y Liu, Z Li, S Pan, C Gong, C Zhou, G Karypis IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022	350	2022
Graph neural networks for graphs with heterophily: A survey X Zheng, Y Wang, Y Liu, M Li, M Zhang, D Jin, PS Yu, S Pan arXiv preprint arXiv:2202.07082, 2022	266	2022
Towards unsupervised deep graph structure learning Y Liu, Y Zheng, D Zhang, H Chen, H Peng, S Pan ACM Web Conference (WWW), 2022	193	2022
Generative and contrastive self-supervised learning for graph anomaly detection Y Zheng, M Jin, Y Liu, L Chi, KT Phan, YPP Chen IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021	146	2021
Federated learning on non-iid graphs via structural knowledge sharing Y Tan, Y Liu, G Long, J Jiang, Q Lu, C Zhang AAAI Conference on Artificial Intelligence (AAAI), 2023	133	2023
Anemone: Graph anomaly detection with multi-scale contrastive learning M Jin, Y Liu, Y Zheng, L Chi, YF Li, S Pan ACM International Conference on Information & Knowledge Management (CIKM), 2021	121	2021
Anomaly detection in dynamic graphs via transformer Y Liu, S Pan, YG Wang, F Xiong, L Wang, Q Chen, VCS Lee IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021	110	2021
Beyond smoothing: Unsupervised graph representation learning with edge heterophily discriminating Y Liu, Y Zheng, D Zhang, VCS Lee, S Pan AAAI Conference on Artificial Intelligence (AAAI), 2023	79	2023
Emerging trends in federated learning: From model fusion to federated X learning S Ji, Y Tan, T Saravirta, Z Yang, Y Liu, L Vasankari, S Pan, G Long, ... International Journal of Machine Learning and Cybernetics, 2024	76	2024
GOOD-D: On unsupervised graph out-of-distribution detection Y Liu, K Ding, H Liu, S Pan ACM International Conference on Web Search and Data Mining (WSDM), 2023	62	2023
Towards self-interpretable graph-level anomaly detection Y Liu, K Ding, Q Lu, F Li, LY Zhang, S Pan Advances in Neural Information Processing Systems (NeurIPS), 2023	42	2023
Learning strong graph neural networks with weak information Y Liu, K Ding, J Wang, V Lee, H Liu, S Pan ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2023	42	2023
Towards data-centric graph machine learning: Review and outlook X Zheng, Y Liu, Z Bao, M Fang, X Hu, AWC Liew, S Pan arXiv preprint arXiv:2309.10979, 2023	28	2023
Integrating graphs with large language models: Methods and prospects S Pan, Y Zheng, Y Liu IEEE Intelligent Systems, 2024	27	2024
From unsupervised to few-shot graph anomaly detection: A multi-scale contrastive learning approach Y Zheng, M Jin, Y Liu, L Chi, KT Phan, YPP Chen arXiv preprint arXiv:2202.05525, 2022	21	2022
PREM: A simple yet effective approach for node-level graph anomaly detection J Pan, Y Liu, Y Zheng, S Pan IEEE International Conference on Data Mining (ICDM), 2023	13	2023
MRD-NETS: multi-scale residual networks with dilated convolutions for classification and clustering analysis of spacecraft electrical signal Y Liu, K Li, Y Zhang, S Song IEEE Access 7, 171584-171597, 2020	11	2020
A novel method of hyperspectral data classification based on transfer learning and deep belief network K Li, M Wang, Y Liu, N Yu, W Lan Applied Sciences 9 (7), 1379, 2020	11	2020
Cyclic label propagation for graph semi-supervised learning Z Li, Y Liu, Z Zhang, S Pan, J Gao, J Bu World Wide Web, 2022	8	2022

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