World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
55
Citations
36129
World Ranking
2917
National Ranking
183

Renaud Lambiotte publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Renaud Lambiotte sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 225 publications — 57th percentile

57% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Renaud Lambiotte D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Renaud Lambiotte sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 55 D-Index — 70th percentile

70% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Renaud Lambiotte is affiliated with the University of Oxford in the United Kingdom. Their research spans multiple fields, predominantly in Physics and Astronomy as well as Computer Science.

Their work focuses on a range of topics including:

  • Complex Network Analysis Techniques
  • Opinion Dynamics and Social Influence
  • Topological and Geometric Data Analysis
  • Nonlinear Dynamics and Pattern Formation
  • Neural dynamics and brain function
  • Functional Brain Connectivity Studies
  • Data Visualization and Analytics

Their subfields of study comprise:

  • Statistical and Nonlinear Physics
  • Computer Networks and Communications
  • Cognitive Neuroscience
  • Computational Theory and Mathematics
  • Sociology and Political Science

Renaud Lambiotte has contributed numerous papers within frequent publication venues, including:

  • arXiv (Cornell University)
  • Physical review. E
  • Communications Physics
  • Proceedings of the International AAAI Conference on Web and Social Media
  • Journal of Physics Complexity

Among their recent papers are:

  • "Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle" (2020, Science Advances)
  • "Socio-Spatial Properties of Online Location-Based Social Networks" (2021, Proceedings of the International AAAI Conference on Web and Social Media)
  • "Multibody interactions and nonlinear consensus dynamics on networked systems" (2020, Physical review. E)
  • "Mobile phone data and COVID-19: Missing an opportunity?" (2020, arXiv (Cornell University))
  • "Metastable oscillatory modes emerge from synchronization in the brain spacetime connectome" (2022, Communications Physics)

Their collaborations include frequent co-authors such as:

  • Yu Tian
  • Fabio Saracco
  • Morten L. Kringelbach
  • Karel Devriendt
  • Gustavo Deco

Renaud Lambiotte has also contributed to book publications, including a title in the Complexity science series:

  • A Guide to Temporal Networks (2020)

Best Publications

  • Fast unfolding of communities in large networks

    Vincent D. Blondel;Jean Loup Guillaume;Jean Loup Guillaume;Renaud Lambiotte;Renaud Lambiotte;Etienne Lefebvre

  • Fast unfolding of communities in large networks

    Vincent D. Blondel;Jean-Loup Guillaume;Jean-Loup Guillaume;Renaud Lambiotte;Renaud Lambiotte;Etienne Lefebvre

  • Modular and hierarchically modular organization of brain networks.

    David Meunier;Renaud Lambiotte;Edward T. Bullmore

  • Multirelational organization of large-scale social networks in an online world

    Michael Szell;Renaud Lambiotte;Stefan Thurner

  • A tale of many cities: universal patterns in human urban mobility.

    Anastasios Noulas;Salvatore Scellato;Renaud Lambiotte;Massimiliano Pontil

  • Hierarchical modularity in human brain functional networks.

    David Meunier;Renaud Lambiotte;Alexander Fornito;Alexander Fornito;Karen D Ersche

  • Line graphs, link partitions, and overlapping communities.

    T. S. Evans;R. Lambiotte

  • Random walks and diffusion on networks

    Naoki Masuda;Mason A. Porter;Mason A. Porter;Renaud Lambiotte

  • Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle.

    Nuria Oliver;Bruno Lepri;Harald Sterly;Renaud Lambiotte;Renaud Lambiotte

  • Socio-Spatial Properties of Online Location-Based Social Networks.

    Salvatore Scellato;Anastasios Noulas;Renaud Lambiotte;Cecilia Mascolo

  • From networks to optimal higher-order models of complex systems

    Renaud Lambiotte;Martin Rosvall;Ingo Scholtes

  • Geographical dispersal of mobile communication networks

    Renaud Lambiotte;Renaud Lambiotte;Vincent D. Blondel;Cristobald de Kerchove;Etienne Huens

  • Laplacian Dynamics and Multiscale Modular Structure in Networks

    R. Lambiotte;J. C. Delvenne;M. Barahona

  • Memory in network flows and its effects on spreading dynamics and community detection

    Martin Rosvall;Alcides V. Esquivel;Andrea Lancichinetti;Jevin D. West

  • Uncovering space-independent communities in spatial networks

    Paul Expert;Tim S. Evans;Vincent D. Blondel;Vincent D. Blondel;Renaud Lambiotte;Renaud Lambiotte

  • Random Walks, Markov Processes and the Multiscale Modular Organization of Complex Networks

    Renaud Lambiotte;Jean-Charles Delvenne;Mauricio Barahona

  • A Guide to Temporal Networks

    Naoki Masuda;Renaud Lambiotte

  • Tracking the Digital Footprints of Personality

    Renaud Lambiotte;Michal Kosinski

  • Fast unfolding of community hierarchies in large networks

    Vincent D. Blondel;Jean-Loup Guillaume;Renaud Lambiotte;Etienne Lefebvre

  • The personality of popular facebook users

    Daniele Quercia;Renaud Lambiotte;David Stillwell;Michal Kosinski

  • Dynamical exploration of the repertoire of brain networks at rest is modulated by psilocybin

    Louis-David Lord;Paul Expert;Selen Atasoy;Leor Roseman

  • The discovery of population differences in network community structure: new methods and applications to brain functional networks in schizophrenia.

    Aaron Alexander-Bloch;Renaud Lambiotte;Ben Roberts;Jay N. Giedd

  • Random Walks, Markov Processes and the Multiscale Modular Organization of Complex Networks

    Renaud Lambiotte;Jean-Charles Delvenne;Mauricio Barahona

  • A Guide to Temporal Networks, 2nd edition

    Naoki Masuda;Renaud Lambiotte

  • Random walks and diffusion on networks

    Naoki Masuda;Mason A. Porter;Mason A. Porter;Renaud Lambiotte

Frequent Co-Authors

Vincent D. Blondel
Vincent D. Blondel Université Catholique de Louvain
Martin Rosvall
Martin Rosvall Umeå University
Cecilia Mascolo
Cecilia Mascolo University of Cambridge
Mauricio Barahona
Mauricio Barahona Imperial College London
Mason A. Porter
Mason A. Porter University of California, Los Angeles
Federico E. Turkheimer
Federico E. Turkheimer King's College London
Naoki Masuda
Naoki Masuda University at Buffalo, State University of New York
Oliver D. Howes
Oliver D. Howes King's College London
Sidney Redner
Sidney Redner Santa Fe Institute
Michal Kosinski
Michal Kosinski Stanford University

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