World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
72
Citations
19121
World Ranking
1693
National Ranking
31

Gao Cong 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 Gao Cong 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: 226 publications — 55th percentile

55% of scientists in this discipline score the same or lower.

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

Gao Cong 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 Gao Cong 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: 72 D-Index — 89th percentile

89% of scientists in this discipline score the same or lower.

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

Overview

Gao Cong is affiliated with Nanyang Technological University in Singapore. Their research primarily spans the field of Computer Science with a strong focus on several subfields including Signal Processing, Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Networks and Communications, and Information Systems. The breadth of their research reflects a diversified interest in both theoretical and applied aspects of computing.

Their recent work covers a variety of topics, particularly in Data Management and Algorithms, Advanced Database Systems and Queries, Time Series Analysis and Forecasting, Human Mobility and Location-Based Analysis, Geographic Information Systems Studies, Traffic Prediction and Management Techniques, and Data Mining Algorithms and Applications.

Among the recent papers authored by Gao Cong are:

  • Learning Dynamics and Heterogeneity of Spatial-Temporal Graph Data for Traffic Forecasting, 2021, IEEE Transactions on Knowledge and Data Engineering
  • A Survey on Trajectory Data Management, Analytics, and Learning, 2021, ACM Computing Surveys
  • Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis, 2024, IEEE Transactions on Knowledge and Data Engineering
  • Cardinality Estimation in DBMS, 2021, Proceedings of the VLDB Endowment
  • QueryFormer, 2022, Proceedings of the VLDB Endowment

Gao Cong frequently collaborates with several co-authors, including Cheng Long, Zhifeng Bao, Xiucheng Li, Weiming Huang, and Yile Chen, who have co-authored multiple publications with them.

Their research has been published across various prominent venues such as:

  • arXiv (Cornell University)
  • Proceedings of the VLDB Endowment
  • Proceedings of the ACM on Management of Data
  • IEEE Transactions on Knowledge and Data Engineering
  • Proceedings of the 2022 International Conference on Management of Data

Best Publications

  • Time-aware point-of-interest recommendation

    Quan Yuan;Gao Cong;Zongyang Ma;Aixin Sun

  • Efficient retrieval of the top-k most relevant spatial web objects

    Gao Cong;Christian S. Jensen;Dingming Wu

  • Community-based greedy algorithm for mining top-K influential nodes in mobile social networks

    Yu Wang;Gao Cong;Guojie Song;Kunqing Xie

  • Learning Dynamics and Heterogeneity of Spatial-Temporal Graph Data for Traffic Forecasting

    Shengnan Guo;Youfang Lin;Huaiyu Wan;Xiucheng Li

  • Global Context Enhanced Graph Neural Networks for Session-based Recommendation

    Ziyang Wang;Wei Wei;Gao Cong;Xiao-Li Li

  • Improving data quality: consistency and accuracy

    Gao Cong;Wenfei Fan;Floris Geerts;Xibei Jia

  • Mining significant semantic locations from GPS data

    Xin Cao;Gao Cong;Christian S. Jensen

  • Personalized ranking metric embedding for next new POI recommendation

    Shanshan Feng;Xutao Li;Yifeng Zeng;Gao Cong

  • Collective spatial keyword querying

    Xin Cao;Gao Cong;Christian S. Jensen;Beng Chin Ooi

  • Spatial keyword query processing: an experimental evaluation

    Lisi Chen;Gao Cong;Christian S. Jensen;Dingming Wu

  • Rank-GeoFM: A Ranking based Geographical Factorization Method for Point of Interest Recommendation

    Xutao Li;Gao Cong;Xiao-Li Li;Tuan-Anh Nguyen Pham

  • On Predicting the Popularity of Newly Emerging Hashtags in Twitter

    Zongyang Ma;Aixin Sun;Gao Cong

  • Finding question-answer pairs from online forums

    Gao Cong;Long Wang;Chin-Yew Lin;Young-In Song

  • Simulated annealing based influence maximization in social networks

    Qingye Jiang;Guojie Song;Gao Cong;Yu Wang

  • Carpenter: finding closed patterns in long biological datasets

    Feng Pan;Gao Cong;Anthony K. H. Tung;Jiong Yang

  • An experimental evaluation of point-of-interest recommendation in location-based social networks

    Yiding Liu;Tuan-Anh Nguyen Pham;Gao Cong;Quan Yuan

  • Graph-based Point-of-interest Recommendation with Geographical and Temporal Influences

    Quan Yuan;Gao Cong;Aixin Sun

  • Who, where, when and what: discover spatio-temporal topics for twitter users

    Quan Yuan;Gao Cong;Zongyang Ma;Aixin Sun

  • Deep Representation Learning for Trajectory Similarity Computation

    Xiucheng Li;Kaiqi Zhao;Gao Cong;Christian S. Jensen

  • Retrieving top-k prestige-based relevant spatial web objects

    Xin Cao;Gao Cong;Christian S. Jensen

  • POI2Vec: Geographical Latent Representation for Predicting Future Visitors

    Shanshan Feng;Gao Cong;Bo An;Yeow Meng Chee

Frequent Co-Authors

Christian S. Jensen
Christian S. Jensen Aalborg University
Quan Yuan
Quan Yuan Dow Chemical (United States)
Bin Cui
Bin Cui Peking University
Aixin Sun
Aixin Sun Nanyang Technological University
Xiaoli Li
Xiaoli Li Singapore University of Technology and Design
Kian-Lee Tan
Kian-Lee Tan National University of Singapore
Wenfei Fan
Wenfei Fan University of Edinburgh
Anthony K. H. Tung
Anthony K. H. Tung National University of Singapore
Chin-Yew Lin
Chin-Yew Lin Microsoft Research Asia (China)
Shuai Ma
Shuai Ma Beihang University

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