World's Best Scientists 2026 revealed!
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Computer Science
Australia
2025

D-Index & Metrics

Computer Science

D-Index
71
Citations
35065
World Ranking
1730
National Ranking
236

Research.com Recognitions

  • 2025 - Research.com Computer Science in Australia Leader Award
  • 2023 - Research.com Computer Science in Australia Leader Award
  • 2022 - Research.com Computer Science in Australia Leader Award

Overview

Chengqi Zhang is affiliated with the University of Technology Sydney in Australia and has a significant body of work primarily focused on computer science, with a particular emphasis on artificial intelligence. Their research spans multiple subfields including molecular biology, computer vision and pattern recognition, electrical and electronic engineering, and materials chemistry.

The scientist's main research topics include:

  • Topic Modeling
  • Multimodal Machine Learning Applications
  • Privacy-Preserving Technologies in Data
  • Magnetic Properties and Synthesis of Ferrites
  • Anomaly Detection Techniques and Applications
  • Advanced Graph Neural Networks
  • Domain Adaptation and Few-Shot Learning

Some of Chengqi Zhang's recent papers are:

  • "FedProto: Federated Prototype Learning across Heterogeneous Clients", 2022, Proceedings of the AAAI Conference on Artificial Intelligence
  • "Image super-resolution with an enhanced group convolutional neural network", 2022, Neural Networks
  • "Bidirectional Spatial-Temporal Adaptive Transformer for Urban Traffic Flow Forecasting", 2022, IEEE Transactions on Neural Networks and Learning Systems
  • "Federated Learning on Non-IID Graphs via Structural Knowledge Sharing", 2023, Proceedings of the AAAI Conference on Artificial Intelligence
  • "A cross Transformer for image denoising", 2023, Information Fusion

Frequent collaborators in their work include Guodong Long, Jing Jiang (two distinct author profiles with substantial collaboration counts), Tianyi Zhou, and Rui Tang.

Chengqi Zhang's publications are frequently found in:

  • arXiv (Cornell University)
  • SSRN Electronic Journal
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • IEEE Transactions on Knowledge and Data Engineering
  • Research Square (Research Square)

In addition to journal articles and conference papers, Zhang has contributed to book publications through Springer Science+Business Media, notably in volumes titled "Advances in Knowledge Discovery and Data Mining" published in 2022.

Best Publications

  • A Comprehensive Survey on Graph Neural Networks

    Zonghan Wu;Shirui Pan;Fengwen Chen;Guodong Long

  • Graph WaveNet for Deep Spatial-Temporal Graph Modeling

    Zonghan Wu;Shirui Pan;Guodong Long;Jing Jiang

  • Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks

    Zonghan Wu;Shirui Pan;Guodong Long;Jing Jiang

  • Adversarially regularized graph autoencoder for graph embedding

    Shirui Pan;Ruiqi Hu;Guodong Long;Jing Jiang

  • DiSAN: Directional Self-Attention Network for RNN/CNN-free Language Understanding

    Tao Shen;Tianyi Zhou;Guodong Long;Jing Jiang

  • Data preparation for data mining

    Shichao Zhang;Chengqi Zhang;Qiang Yang

  • Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

    Longbing Cao;Chengqi Zhang;Thorsten Joachims;Geoff Webb

  • Network Representation Learning: A Survey

    Daokun Zhang;Jie Yin;Xingquan Zhu;Chengqi Zhang

  • Association Rule Mining: Models and Algorithms

    Chengqi Zhang;Shichao Zhang

  • FedProto: Federated Prototype Learning across Heterogeneous Clients

    Unknown

  • Efficient mining of both positive and negative association rules

    Xindong Wu;Chengqi Zhang;Shichao Zhang

  • Attributed Graph Clustering: a Deep Attentional Embedding approach

    Chun Wang;Shirui Pan;Ruiqi Hu;Guodong Long

  • Tri-party deep network representation

    Shirui Pan;Jia Wu;Xingquan Zhu;Chengqi Zhang

  • Conference on Neural Information Processing Systems

    L Liu;T Zhou;Guodong Long;Jing Jiang

  • Learning Graph Embedding With Adversarial Training Methods

    Shirui Pan;Ruiqi Hu;Sai-Fu Fung;Guodong Long

  • Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support

    Xiaowei Yan;Chengqi Zhang;Shichao Zhang

  • Dynamic Affinity Graph Construction for Spectral Clustering Using Multiple Features

    Zhihui Li;Feiping Nie;Xiaojun Chang;Yi Yang

  • Mining Both Positive and Negative Association Rules

    Xindong Wu;Chengqi Zhang;Shichao Zhang

  • Federated Learning for Open Banking

    Guodong Long;Yue Tan;Jing Jiang;Chengqi Zhang

  • Compound Rank- $k$ Projections for Bilinear Analysis

    Xiaojun Chang;Feiping Nie;Sen Wang;Yi Yang

  • Support vector machines based on K-means clustering for real-time business intelligence systems

    Jiaqi Wang;Xindong Wu;Chengqi Zhang

  • Proceedings of the 10th IEEE International Conference on Data Mining (ICDM)

    Geoffrey Webb;Bing Liu;Chengqi Zhang;Dimitrios Gunopulos

Frequent Co-Authors

Longbing Cao
Longbing Cao University of Technology Sydney
Guodong Long
Guodong Long University of Technology Sydney
Xingquan Zhu
Xingquan Zhu Florida Atlantic University
Jing Jiang
Jing Jiang Singapore Management University
Shirui Pan
Shirui Pan Griffith University
Jia Wu
Jia Wu Macquarie University
Xindong Wu
Xindong Wu Hefei University of Technology
Philip S. Yu
Philip S. Yu University of Illinois at Chicago
Peng Zhang
Peng Zhang Huazhong University of Science and Technology
Ivor W. Tsang
Ivor W. Tsang Agency for Science, Technology and Research

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