World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
5197
World Ranking
12600
National Ranking
5105

Yu-Ru Lin 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 Yu-Ru Lin 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: 149 publications — 26th percentile

26% of scientists in this discipline score the same or lower.

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

Yu-Ru Lin 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 Yu-Ru Lin 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

Yu-Ru Lin is affiliated with the University of Pittsburgh in the United States. The scientific work focuses primarily on the social sciences, with significant contributions to subfields including sociology and political science, artificial intelligence, communication, statistical and nonlinear physics, and renewable energy, sustainability, and the environment.

The main research topics covered by Yu-Ru Lin include:

  • Social Media and Politics
  • Misinformation and Its Impacts
  • Opinion Dynamics and Social Influence
  • Complex Network Analysis Techniques
  • Aquaculture disease management and microbiota
  • Electrocatalysts for Energy Conversion
  • Advanced battery technologies research

Yu-Ru Lin has contributed to various journals and conference venues. The most frequent publication venues include:

  • arXiv (Cornell University)
  • Proceedings of the International AAAI Conference on Web and Social Media
  • Frontiers in Marine Science
  • ACM Transactions on Interactive Intelligent Systems
  • Advanced Energy and Sustainability Research

Recent scholarly papers by Yu-Ru Lin and collaborators are:

  • #Bigbirds Never Die: Understanding Social Dynamics of Emergent Hashtags, 2021, Proceedings of the International AAAI Conference on Web and Social Media
  • More Voices Than Ever? Quantifying Media Bias in Networks, 2021, Proceedings of the International AAAI Conference on Web and Social Media
  • The dynamics of Twitter users' gun narratives across major mass shooting events, 2020, Humanities and Social Sciences Communications

Additional notable papers authored by others but relevant within the broader research field include:

  • Data-Driven Computational Social Science: A Survey, 2020, Big Data Research
  • Accuracy of long-form data in the Taiwan cancer registry, 2021, Journal of the Formosan Medical Association

Yu-Ru Lin collaborates frequently with several researchers, including:

  • Wen-Ting Chung
  • Yongsu Ahn
  • Huai-Ting Huang
  • Yeh-Fang Hu
  • Yan-Gu Lin

Best Publications

  • Facetnet: a framework for analyzing communities and their evolutions in dynamic networks

    Yu-Ru Lin;Yun Chi;Shenghuo Zhu;Hari Sundaram

  • Analyzing communities and their evolutions in dynamic social networks

    Yu-Ru Lin;Yun Chi;Shenghuo Zhu;Hari Sundaram

  • Recipe recommendation using ingredient networks

    Chun-Yuen Teng;Yu-Ru Lin;Lada A. Adamic

  • How Does the Data Sampling Strategy Impact the Discovery of Information Diffusion in Social Media

    Munmun De Choudhury;Yu Ru Lin;Hari Sundaram;K. Selçuk Candan

  • MetaFac: community discovery via relational hypergraph factorization

    Yu-Ru Lin;Jimeng Sun;Paul Castro;Ravi Konuru

  • Whisper: Tracing the Spatiotemporal Process of Information Diffusion in Real Time

    Nan Cao;Yu-Ru Lin;Xiaohua Sun;D. Lazer

  • #FluxFlow: Visual Analysis of Anomalous Information Spreading on Social Media

    Jian Zhao;Nan Cao;Zhen Wen;Yale Song

  • FacetAtlas: Multifaceted Visualization for Rich Text Corpora

    Nan Cao;Jimeng Sun;Yu-Ru Lin;D Gotz

  • Rising tides or rising stars?: Dynamics of shared attention on Twitter during media events.

    Yu Ru Lin;Brian Keegan;Drew Margolin;David Lazer

  • TargetVue: Visual Analysis of Anomalous User Behaviors in Online Communication Systems

    Nan Cao;Conglei Shi;Sabrina Lin;Jie Lu

  • FairSight: Visual Analytics for Fairness in Decision Making

    Yongsu Ahn;Yu-Ru Lin

  • Tracking employment shocks using mobile phone data

    Jameson Lawrence Toole;Yu-Ru Lin;Erich Muehlegger;Daniel Shoag

  • Discovery of Blog Communities based on Mutual Awareness

    Yu-Ru Lin;Hari Sundaram;Yun Chi;Jun Tatemura

  • Voila: Visual Anomaly Detection and Monitoring with Streaming Spatiotemporal Data

    Nan Cao;Chaoguang Lin;Qiuhan Zhu;Yu-Ru Lin

  • Blog Community Discovery and Evolution Based on Mutual Awareness Expansion

    Yu-Ru Lin;Hari Sundaram;Yun Chi;Junichi Tatemura

  • #Bigbirds Never Die: Understanding Social Dynamics of Emergent Hashtags

    Yu-Ru Lin;Drew Margolin;Brian Keegan;Andrea Baronchelli

  • Splog detection using self-similarity analysis on blog temporal dynamics

    Yu-Ru Lin;Hari Sundaram;Yun Chi;Junichi Tatemura

  • The ripple of fear, sympathy and solidarity during the Boston bombings

    Yu-Ru Lin;Drew Margolin

  • Connecting content to community in social media via image content, user tags and user communication

    Munmun De Choudhury;Hari Sundaram;Yu-Ru Lin;Ajita John

  • SocialHelix: visual analysis of sentiment divergence in social media

    Nan Cao;Lu Lu;Yu-Ru Lin;Fei Wang

  • Voices of victory: a computational focus group framework for tracking opinion shift in real time

    Yu-Ru Lin;Drew Margolin;Brian Keegan;David Lazer

  • More Voices Than Ever? Quantifying Media Bias in Networks

    Yu-Ru Lin;James P. Bagrow;David Lazer

  • Data-driven Computational Social Science: A Survey

    Jun Zhang;Wei Wang;Feng Xia;Yu-Ru Lin

  • Twitter in academic events

    Denis Parra;Christoph Trattner;Diego Gómez;Matías Hurtado

  • #Bigbirds Never Die: Understanding Social Dynamics of Emergent Hashtag

    Yu-Ru Lin;Drew Margolin;Brian Keegan;Andrea Baronchelli

Frequent Co-Authors

Hari Sundaram
Hari Sundaram University of Illinois at Urbana-Champaign
Nan Cao
Nan Cao Tongji University
David Lazer
David Lazer Northeastern University
Belle L. Tseng
Belle L. Tseng Apple (United States)
Yun Chi
Yun Chi Robinhood
Jimeng Sun
Jimeng Sun University of Illinois at Urbana-Champaign
Munmun De Choudhury
Munmun De Choudhury Georgia Institute of Technology
Hanghang Tong
Hanghang Tong University of Illinois at Urbana-Champaign
Karen S. Quigley
Karen S. Quigley Northeastern University
Lisa Feldman Barrett
Lisa Feldman Barrett Northeastern 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

If you’re interested in studying Computer Science in the USA, there are several flexible online education options to consider. Online online associates degrees in computer science or IT are a great entry point, offering foundational skills and paving the way toward further study or immediate job opportunities.

For those looking to upskill quickly, many choose certificate programs that pay well. These short courses can be completed online and are designed to meet specific job market needs, making them ideal for career changers or professionals seeking advancement.

If you already have a bachelor’s degree, pursuing one of the quickest cheapest masters degree programs can save both time and money. Many of these accelerated master’s options are available online, offering flexibility for busy learners.

Finally, it’s worth considering which master's degree is most in demand in usa to align your studies with high-growth fields. Computer science, data science, and cybersecurity consistently appear among the most desirable credentials for employers.

Best Scientists Citing Yu-Ru Lin

Trending Scientists

Recently Published Articles