World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
4351
World Ranking
12726
National Ranking
808

Danushka Bollegala 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 Danushka Bollegala 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: 273 publications — 68th percentile

68% of scientists in this discipline score the same or lower.

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

Danushka Bollegala 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 Danushka Bollegala 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: 33 D-Index — 13th percentile

13% of scientists in this discipline score the same or lower.

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

Overview

Danushka Bollegala is affiliated with the University of Liverpool in the United Kingdom. Their research primarily spans the field of Computer Science, with a strong focus on Artificial Intelligence. Additional subfields of study include Molecular Biology, Materials Chemistry, Cognitive Neuroscience, and Computer Vision and Pattern Recognition.

The scientist's work covers a wide range of topics within Artificial Intelligence and related areas. Key topics include:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Speech and dialogue systems
  • Advanced Graph Neural Networks
  • Text and Document Classification Technologies
  • Text Readability and Simplification
  • Hate Speech and Cyberbullying Detection

Danushka Bollegala has contributed to academic literature through recent papers published in well-known venues, including:

  • "Explanation in AI and law: Past, present and future," 2020, Artificial Intelligence
  • "DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach," 2020, Journal of Cheminformatics
  • "Unmasking the Mask - Evaluating Social Biases in Masked Language Models," 2022, Proceedings of the AAAI Conference on Artificial Intelligence
  • "Gender Bias in Masked Language Models for Multiple Languages," 2022, Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
  • "Random projections and kernelised leave one cluster out cross validation: universal baselines and evaluation tools for supervised machine learning of material properties," 2022, Digital Discovery

Their frequent publication venues include:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • bioRxiv (Cold Spring Harbor Laboratory)
  • SSRN Electronic Journal
  • Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Danushka Bollegala collaborates regularly with several co-authors. Frequent collaborators include:

  • James O'Neill
  • Masahiro Kaneko
  • Yi Zhou
  • Naoaki Okazaki

Best Publications

  • Measuring Semantic Similarity between Words Using Web Search Engines

    Danushka Bollegala;Yutaka Matsuo;Mitsuru Ishizuka

  • Cross-Domain Sentiment Classification Using a Sentiment Sensitive Thesaurus

    D. Bollegala;D. Weir;J. Carroll

  • A Web Search Engine-Based Approach to Measure Semantic Similarity between Words

    D. Bollegala;Y. Matsuo;M. Ishizuka

  • Using Multiple Sources to Construct a Sentiment Sensitive Thesaurus for Cross-Domain Sentiment Classification

    Danushka Bollegala;David Weir;John Carroll

  • Social media and pharmacovigilance: A review of the opportunities and challenges

    Richard Sloane;Orod Osanlou;Orod Osanlou;David Lewis;Danushka Bollegala

  • Relational duality: unsupervised extraction of semantic relations between entities on the web

    Danushka Tarupathi Bollegala;Yutaka Matsuo;Mitsuru Ishizuka

  • Explanation in AI and law: Past, present and future

    Katie Atkinson;Trevor J. M. Bench-Capon;Danushka Bollegala

  • Cross-Domain Sentiment Classification Using Sentiment Sensitive Embeddings

    Danushka Bollegala;Tingting Mu;John Yannis Goulermas

  • A bottom-up approach to sentence ordering for multi-document summarization

    Danushka Bollegala;Naoaki Okazaki;Mitsuru Ishizuka

  • Gender-preserving Debiasing for Pre-trained Word Embeddings

    Masahiro Kaneko;Danushka Bollegala

  • Frustratingly Easy Meta-Embedding -- Computing Meta-Embeddings by Averaging Source Word Embeddings

    Joshua Coates;Danushka Bollegala

  • Measuring the similarity between implicit semantic relations from the web

    Danushka T. Bollegala;Yutaka Matsuo;Mitsuru Ishizuka

  • Debiasing Pre-trained Contextualised Embeddings.

    Masahiro Kaneko;Danushka Bollegala

  • Spinning multiple social networks for semantic web

    Yutaka Matsuo;Masahiro Hamasaki;Yoshiyuki Nakamura;Takuichi Nishimura

  • Unsupervised Cross-Domain Word Representation Learning

    Danushka Bollegala;Takanori Maehara;Ken-ichi Kawarabayashi

  • Automatic Discovery of Personal Name Aliases from the Web

    D Bollegala;Y Matsuo;M Ishizuka

  • “Touching to See” and “Seeing to Feel”: Robotic Cross-modal Sensory Data Generation for Visual-Tactile Perception

    Jet-Tsyn Lee;Danushka Bollegala;Shan Luo

  • DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach.

    Yash Khemchandani;Yash Khemchandani;Stephen O’Hagan;Soumitra Samanta;Neil Swainston

  • Joint word representation learning using a corpus and a semantic lexicon

    Danushka Bollegala;Alsuhaibani Mohammed;Takanori Maehara;Ken-ichi Kawarabayashi

  • Disambiguating Personal Names on the Web using Automatically Extracted Key Phrases

    Danushka Bollegala;Yutaka Matsuo;Mitsuru Ishizuka

  • A Relational Model of Semantic Similarity between Words using Automatically Extracted Lexical Pattern Clusters from the Web

    Danushka Bollegala;Yutaka Matsuo;Mitsuru Ishizuka

  • Learning Word Meta-Embeddings by Autoencoding

    Danushka Bollegala;Cong Bao

  • An adaptive differential evolution algorithm

    Nasimul Noman;Danushka Bollegala;Hitoshi Iba

  • Spatio-temporal Attention Model for Tactile Texture Recognition

    Guanqun Cao;Yi Zhou;Danushka Bollegala;Shan Luo

  • "Touching to See" and "Seeing to Feel": Robotic Cross-modal SensoryData Generation for Visual-Tactile Perception

    Jet-Tsyn Lee;Danushka Bollegala;Shan Luo

Frequent Co-Authors

Mitsuru Ishizuka
Mitsuru Ishizuka University of Tokyo
Yutaka Matsuo
Yutaka Matsuo University of Tokyo
Frans Coenen
Frans Coenen University of Liverpool
Ken-ichi Kawarabayashi
Ken-ichi Kawarabayashi National Institute of Informatics
Naoaki Okazaki
Naoaki Okazaki Tokyo Institute of Technology
Hitoshi Iba
Hitoshi Iba University of Tokyo
Paula R Williamson
Paula R Williamson University of Liverpool
Simon Parsons
Simon Parsons University of Lincoln
Kiyoharu Aizawa
Kiyoharu Aizawa University of Tokyo
Douglas B. Kell
Douglas B. Kell University of Liverpool

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