Chuan Zhou

Active 2000–2026

572
Papers
22,655
Citations
81
h-index
294
i10-index

Citations

Citations per year for Chuan Zhou1965: 1 citations2000: 1 citations2001: 2 citations2002: 1 citations2003: 2 citations2004: 6 citations2005: 14 citations2006: 41 citations2007: 68 citations2008: 124 citations2009: 140 citations2010: 80 citations2011: 130 citations2012: 110 citations2013: 122 citations2014: 208 citations2015: 294 citations2016: 293 citations2017: 286 citations2018: 325 citations2019: 643 citations2020: 725 citations2021: 1,071 citations2022: 1,081 citations2023: 1,285 citations2024: 1,671 citations2025: 1,329 citations2026: 333 citations2027: 2 citations1966–1999: no citations, so these years are not shown

Citation sources

Countries

World map of the countries and regions citing this authorChina: 3,801 citing papers, 30.4% of this breakdownUnited States: 2,564 citing papers, 20.5% of this breakdownAustralia: 715 citing papers, 5.7% of this breakdownUnited Kingdom: 537 citing papers, 4.3% of this breakdownCanada: 422 citing papers, 3.4% of this breakdownIndia: 315 citing papers, 2.5% of this breakdownGermany: 287 citing papers, 2.3% of this breakdownHong Kong: 274 citing papers, 2.2% of this breakdownItaly: 219 citing papers, 1.8% of this breakdownSouth Korea: 219 citing papers, 1.7% of this breakdownNetherlands: 173 citing papers, 1.4% of this breakdownFrance: 172 citing papers, 1.4% of this breakdown
0%30.4%Other 22.4%

Fields

  • Computer Science42%
  • Medicine22.4%
  • Physics and Astronomy7.3%
  • Social Sciences6.6%
  • Biochemistry, Genetics and Molecular Biology4.6%
  • Engineering4.6%
  • Other12.5%

Topics

  • Advanced Graph Neural Networks6.4%
  • Complex Network Analysis Techniques4.5%
  • AI in cancer detection3.5%
  • Radiomics and Machine Learning in Medical Imaging2.6%
  • Recommender Systems and Techniques2.1%
  • Anomaly Detection Techniques and Applications1.9%
  • Other79%

Coauthors

All papers

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  1. Graph Self-Supervised Learning: A Survey

    Authors: , , , , , , - IEEE Transactions on Knowledge and Data Engineering, IEEE Trans. Knowl. Data Eng. 2022 cited by 495

  2. Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning

    Authors: , , , , , - IEEE Transactions on Neural Networks and Learning Systems, IEEE Trans. Neural Networks Learn. Syst. 2021 cited by 448

  3. Deep Learning in Medical Image Analysis

    Authors: , , , - Advances in experimental medicine and biology 2020 cited by 735

  4. A Comprehensive Survey on Community Detection With Deep Learning

    Authors: , , , , , , , , , , , - IEEE Transactions on Neural Networks and Learning Systems, IEEE Trans. Neural Networks Learn. Syst. 2022 cited by 333

  5. H2-FDetector: A GNN-based Fraud Detector with Homophilic and Heterophilic Connections

    Authors: , , , , , - Web Conference 2022, WWW 2022 cited by 136

  6. eFraudCom: An E-commerce Fraud Detection System via Competitive Graph Neural Networks

    Authors: , , , , , , - ACM Transactions on Information Systems, ACM Trans. Inf. Syst. 2022 cited by 166

  7. Unsupervised Domain Adaptive Graph Convolutional Networks

    Authors: , , , , - Web Conference 2020, WWW 2020 cited by 148

  8. FRAUDRE: Fraud Detection Dual-Resistant to Graph Inconsistency and Imbalance

    Authors: , , , , , , - IEEE International Conference on Data Mining (ICDM) 2021 cited by 110

  9. Deep Learning for Community Detection: Progress, Challenges and Opportunities

    Authors: , , , , , , , , - Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020 cited by 191

  10. Graph Neural Architecture Search

    Authors: , , , , - Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020 cited by 154

  11. Relation Structure-Aware Heterogeneous Graph Neural Network

    Authors: , , , , - IEEE International Conference on Data Mining (ICDM) 2019 cited by 122

  12. Online Active Learning for Drifting Data Streams

    Authors: , , , , , , - IEEE Transactions on Neural Networks and Learning Systems, IEEE Trans. Neural Networks Learn. Syst. 2021 cited by 80

  13. Exploratory Adversarial Attacks on Graph Neural Networks for Semi-Supervised Node Classification

    Authors: , , , , , , - Pattern Recognition, Pattern Recognit. 2022 cited by 64

  14. Flow2GNN: Flexible Two-Way Flow Message Passing for Enhancing GNNs Beyond Homophily

    Authors: , , , , , , , - IEEE Transactions on Cybernetics, IEEE Trans. Cybern. 2024 cited by 62

  15. A Comprehensive Survey on Graph Anomaly Detection With Deep Learning

    Authors: , , , , , , , - IEEE Transactions on Knowledge and Data Engineering, IEEE Trans. Knowl. Data Eng. 2021 cited by 76

  16. HGNAS++: Efficient Architecture Search for Heterogeneous Graph Neural Networks

    Authors: , , , , , , - IEEE Transactions on Knowledge and Data Engineering, IEEE Trans. Knowl. Data Eng. 2023 cited by 45

  17. LLM-based Agents Suffer from Hallucinations: A Survey of Taxonomy, Methods, and Directions

    Authors: , , , , , , , , , , , , , , , , , , , , , , , - ArXiv.org, CoRR 2025 cited by 33

  18. Rethinking pre-training on medical imaging

    Authors: , , , - Journal of Visual Communication and Image Representation, J. Vis. Commun. Image Represent. 2021 cited by 85

  19. DAGAD: Data Augmentation for Graph Anomaly Detection

    Authors: , , , , , , , , , - IEEE International Conference on Data Mining (ICDM) 2022 cited by 48

  20. One-Shot Neural Architecture Search: Maximising Diversity to Overcome Catastrophic Forgetting

    Authors: , , , , , , - IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Trans. Pattern Anal. Mach. Intell. 2020 cited by 81

  21. GraphNAS++: Distributed Architecture Search for Graph Neural Networks

    Authors: , , , , , , , - IEEE Transactions on Knowledge and Data Engineering, IEEE Trans. Knowl. Data Eng. 2022 cited by 38

  22. Reasoning Like Human: Hierarchical Reinforcement Learning for Knowledge Graph Reasoning

    Authors: , , , , - Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020 cited by 90

  23. Deep graph level anomaly detection with contrastive learning

    Authors: , , , , , , , , - Scientific Reports 2022 cited by 38

  24. Deep learning algorithm-based multimodal MRI radiomics and pathomics data improve prediction of bone metastases in primary prostate cancer

    Authors: , , , , , , , , - Journal of Cancer Research and Clinical Oncology 2024 cited by 54