World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
53
Citations
9878
World Ranking
4878
National Ranking
146

Jia Wu publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Jia Wu sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 217 publications — 52nd percentile

52% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Jia Wu D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Jia Wu sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 53 D-Index — 67th percentile

67% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Jia Wu is affiliated with Macquarie University in Australia and has a significant body of research within the field of Computer Science. Their work prominently engages with subfields including Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Statistical and Nonlinear Physics, and Computer Networks and Communications.

The scientist's main research topics involve Advanced Graph Neural Networks, Complex Network Analysis Techniques, Recommender Systems and Techniques, Topic Modeling, Anomaly Detection Techniques and Applications, Privacy-Preserving Technologies in Data, and Misinformation and Its Impacts.

Jia Wu has authored numerous papers published in high-impact venues. Recent notable publications include:

  • A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning, 2021, IEEE Transactions on Knowledge and Data Engineering
  • A comprehensive survey on pretrained foundation models: a history from BERT to ChatGPT, 2024, International Journal of Machine Learning and Cybernetics
  • A Decomposition Dynamic graph convolutional recurrent network for traffic forecasting, 2023, Pattern Recognition
  • A Comprehensive Survey on Automatic Knowledge Graph Construction, 2023, ACM Computing Surveys
  • eFraudCom: An E-commerce Fraud Detection System via Competitive Graph Neural Networks, 2022, ACM Transactions on Information Systems

Frequent collaborators include Jian Yang, Philip S. Yu, Shan Xue, Hao Peng, and Chuan Zhou, contributing to a consistent coauthorship network.

The scientist's works are often published in venues such as arXiv (Cornell University), IEEE Transactions on Knowledge and Data Engineering, Neural Networks, IEEE Transactions on Big Data, and IEEE Transactions on Neural Networks and Learning Systems.

Best Publications

  • Training deep neural networks on imbalanced data sets

    Shoujin Wang;Wei Liu;Jia Wu;Longbing Cao

  • Stacked Convolutional Denoising Auto-Encoders for Feature Representation

    Bo Du;Wei Xiong;Jia Wu;Lefei Zhang

  • A Comprehensive Survey on Graph Anomaly Detection with Deep Learning

    Xiaoxiao Ma;Jia Wu;Shan Xue;Jian Yang

  • Tri-party deep network representation

    Shirui Pan;Jia Wu;Xingquan Zhu;Chengqi Zhang

  • Infrared and visible image fusion via detail preserving adversarial learning

    Jiayi Ma;Pengwei Liang;Wei Yu;Chen Chen

  • A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning

    Di Jin;Zhizhi Yu;Pengfei Jiao;Shirui Pan

  • Deep Learning for Community Detection: Progress, Challenges and Opportunities

    Fanzhen Liu;Shan Xue;Shan Xue;Jia Wu;Chuan Zhou

  • A Comprehensive Survey on Community Detection with Deep Learning.

    Xing Su;Shan Xue;Fanzhen Liu;Jia Wu

  • A Correlation-Based Feature Weighting Filter for Naive Bayes

    Liangxiao Jiang;Lungan Zhang;Chaoqun Li;Jia Wu

  • SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism

    Qingyun Sun;Jianxin Li;Hao Peng;Jia Wu

  • Bag Constrained Structure Pattern Mining for Multi-Graph Classification

    Jia Wu;Xingquan Zhu;Chengqi Zhang;Philip S. Yu

  • Nonrigid Point Set Registration With Robust Transformation Learning Under Manifold Regularization

    Jiayi Ma;Jia Wu;Ji Zhao;Junjun Jiang

  • Neighborhood-Aware Attentional Representation for Multilingual Knowledge Graphs.

    Qiannan Zhu;Xiaofei Zhou;Jia Wu;Jianlong Tan

  • An unsupervised parameter learning model for RVFL neural network.

    Yongshan Zhang;Jia Wu;Zhihua Cai;Bo Du

  • A Deep Framework for Cross-Domain and Cross-System Recommendations.

    Feng Zhu;Yan Wang;Chaochao Chen;Guanfeng Liu

  • Time series feature learning with labeled and unlabeled data

    Haishuai Wang;Haishuai Wang;Haishuai Wang;Qin Zhang;Jia Wu;Shirui Pan

  • Advances in processing, mining, and learning complex data: from foundations to real-world applications

    Jia Wu;Shirui Pan;Chuan Zhou;Gang Li

  • Boosting for Multi-Graph Classification

    Jia Wu;Shirui Pan;Xingquan Zhu;Zhihua Cai

  • Multi-View Multi-Label Learning With Sparse Feature Selection for Image Annotation

    Yongshan Zhang;Jia Wu;Zhihua Cai;Philip S. Yu

  • Self-adaptive attribute weighting for Naive Bayes classification

    Jia Wu;Shirui Pan;Xingquan Zhu;Zhihua Cai

  • Multi-View Fusion with Extreme Learning Machine for Clustering

    Yongshan Zhang;Jia Wu;Chuan Zhou;Zhihua Cai

  • Attribute Weighting via Differential Evolution Algorithm for Attribute Weighted Naive Bayes (WNB)

    Jia Wu;Zhihua Cai

Frequent Co-Authors

Shirui Pan
Shirui Pan Griffith University
Chengqi Zhang
Chengqi Zhang Hong Kong Polytechnic University
Xingquan Zhu
Xingquan Zhu Florida Atlantic University
Bo Du
Bo Du Wuhan University
Peng Zhang
Peng Zhang Huazhong University of Science and Technology
Philip S. Yu
Philip S. Yu University of Illinois at Chicago
Zhihua Cai
Zhihua Cai China University of Geosciences, Wuhan
Guodong Long
Guodong Long University of Technology Sydney
Jianxin Li
Jianxin Li Tianjin Polytechnic University
Quan Z. Sheng
Quan Z. Sheng Macquarie University

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