World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
44
Citations
23083
World Ranking
7352
National Ranking
234

Guodong Long 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 Guodong Long 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: 171 publications — 35th percentile

35% of scientists in this discipline score the same or lower.

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

Guodong Long 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 Guodong Long 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: 44 D-Index — 48th percentile

48% of scientists in this discipline score the same or lower.

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

Overview

Guodong Long is affiliated with the University of Technology Sydney in Australia and has made contributions primarily in the field of Computer Science, with a focus on Artificial Intelligence. Their research spans several subfields including Computer Vision and Pattern Recognition, Information Systems, Signal Processing, and Statistical and Nonlinear Physics.

The scientist has a broad range of research interests concentrating on topics such as Privacy-Preserving Technologies in Data, Topic Modeling, Domain Adaptation and Few-Shot Learning, Recommender Systems and Techniques, Advanced Graph Neural Networks, Time Series Analysis and Forecasting, and Natural Language Processing Techniques.

Guodong Long's recent papers include:

  • Multi-center federated learning: clients clustering for better personalization (2022), published in World Wide Web
  • FedProto: Federated Prototype Learning across Heterogeneous Clients (2022), Proceedings of the AAAI Conference on Artificial Intelligence
  • Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications (2020), IEEE Transactions on Computational Social Systems
  • Federated Learning on Non-IID Graphs via Structural Knowledge Sharing (2023), Proceedings of the AAAI Conference on Artificial Intelligence
  • Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks (2020), arXiv (Cornell University)

The scientist has collaborated frequently with other researchers including Jing Jiang, Chengqi Zhang, Tao Shen, and Tianyi Zhou.

Guodong Long regularly publishes in venues such as:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • IEEE Transactions on Knowledge and Data Engineering
  • World Wide Web
  • IEEE Transactions on Neural Networks and Learning Systems

In addition to journal and conference articles, Guodong Long has authored books published by Springer Science+Business Media, including:

  • AI 2021: Advances in Artificial Intelligence (2022)
  • Advanced Data Mining and Applications (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

  • FedProto: Federated Prototype Learning across Heterogeneous Clients

    Unknown

  • Attributed Graph Clustering: a Deep Attentional Embedding approach

    Chun Wang;Shirui Pan;Ruiqi Hu;Guodong Long

  • MGAE: Marginalized Graph Autoencoder for Graph Clustering

    Chun Wang;Shirui Pan;Guodong Long;Xingquan Zhu

  • 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

  • Federated Learning for Open Banking

    Guodong Long;Yue Tan;Jing Jiang;Chengqi Zhang

  • Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications

    Shaoxiong Ji;Shirui Pan;Xue Li;Erik Cambria

  • Supervised Learning for Suicidal Ideation Detection in Online User Content

    Shaoxiong Ji;Shaoxiong Ji;Celina Ping Yu;Sai-fu Fung;Shirui Pan

  • Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion

    Bo Wang;Tao Shen;Guodong Long;Tianyi Zhou

  • Learning Private Neural Language Modeling with Attentive Aggregation

    Shaoxiong Ji;Shirui Pan;Guodong Long;Xue Li

  • Optimal cloud resource auto-scaling for web applications

    Jing Jiang;Jie Lu;Guangquan Zhang;Guodong Long

  • Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion

    Bo Wang;Tao Shen;Guodong Long;Tianyi Zhou

  • Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling

    Tao Shen;Tianyi Zhou;Guodong Long;Jing Jiang

  • Scaling-Up Item-Based Collaborative Filtering Recommendation Algorithm Based on Hadoop

    Jing Jiang;Jie Lu;Guangquan Zhang;Guodong Long

  • Bi-directional block self-attention for fast and memory-efficient sequence modeling

    Tao Shen;Tianyi Zhou;Guodong Long;Jing Jiang

  • Rethinking 1D-CNN for Time Series Classification: A Stronger Baseline.

    Wensi Tang;Guodong Long;Lu Liu;Tianyi Zhou

  • Multi-Center Federated Learning

    Ming Xie;Guodong Long;Tao Shen;Tianyi Zhou

Frequent Co-Authors

Chengqi Zhang
Chengqi Zhang Hong Kong Polytechnic University
Shirui Pan
Shirui Pan Griffith University
Lina Yao
Lina Yao Commonwealth Scientific and Industrial Research Organisation
Xue Li
Xue Li University of Queensland
Jia Wu
Jia Wu Macquarie University
Peng Zhang
Peng Zhang Huazhong University of Science and Technology
Daxin Jiang
Daxin Jiang Microsoft (United States)
Xingquan Zhu
Xingquan Zhu Florida Atlantic University
Quan Z. Sheng
Quan Z. Sheng Macquarie University
Yi Chang
Yi Chang Jilin University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Interested in expanding your career opportunities beyond traditional computer science? Many students explore related online programs for added flexibility and expertise. Options like the accelerated computer science degree online can help you earn your qualification more quickly, so you can enter the workforce or pivot your profession sooner.

For those focused on cost-effective education, there are alternatives such as the online mechanical engineering degree and the cheapest online environmental science degree. These programs allow you to gain in-demand technical and engineering skills without the financial burden of traditional on-campus learning.

If you want to diversify further, consider exploring different science pathways. Understanding what can I do with an environmental science degree can open doors to roles in research, sustainability, and policy, complementing your computer science background. Exploring these options can greatly enhance your employability and adaptability in today’s technology-driven job market.

Best Scientists Citing Guodong Long

Trending Scientists

Recently Published Articles