Renaud Lambiotte

Renaud Lambiotte
Born
Namur, Belgium
Alma materUniversité libre de Bruxelles (MS, PhD)
OccupationsMathematician, Physicist
Known forComplex networks, Community detection
Scientific career
InstitutionsUniversity of Oxford, Somerville College
Thesis Inelastic Gases: A Paradigm for Far-from-equilibrium Systems  (2004)
Doctoral advisorLeon Brenig

Renaud Lambiotte is a Belgian mathematician and physicist who is currently the Professor of Networks and Nonlinear Systems at the Mathematical Institute, University of Oxford, and a tutorial fellow at Somerville College, University of Oxford.[1] He has contributed to the field of complex networks. He became a fellow of the Network Science Society in 2025.[2]

Career

Born in Namur, Belgium, Lambiotte went on to study physics at Université libre de Bruxelles, graduating with a Master's degree.[3] He completed his PhD in 2004 with a thesis entitled Inelastic Gases: A Paradigm for Far-from-equilibrium Systems, supervised by Léon Brenig.[4] Following this, Lambiotte completed post-docs at ENS Lyon, the University of Liège, UC Louvain, and Imperial College London where he pivoted to the study of complex networks.[1][3][5] In 2011, he joined the faculty at the Department of Mathematics at the University of Namur and the Namur Institute For Complex Systems as an Assistant Professor, becoming Professor in 2014. In 2017, he moved to the Mathematical Institute at the University of Oxford as an Associate Professor, becoming Professor in 2020.[1]

In 2015, he shared the Verdickt-Rijdams Prize from the Académie royale de langue et de littérature françaises de Belgique for La tortue de Zénon, a book collection on the relations between science and literature.[6]

Research

Lambiotte has made contributions to both theoretical and applied topics in network science. His research has focused on network dynamics with applications to brain networks and social networks.[1] He is a co-author of the books A Guide to Temporal Networks with Naoki Masuda[7] and Modularity and Dynamics on Complex Networks with Michael Schaub.[8]

Community detection

Lambiotte has made contributions to the problem of community detection in complex networks. Whilst at UCLouvain, he was a co-author of the Louvain method,[9] an algorithm for maximising modularity, which has gone on to be to cited more than 20,000 times.[10]

Since then, he has made additional contributions such as the use of random walks and Markov chains to discover community structure, including approaches to directed, signed, higher-order and temporal networks.[11][12]

Random walks

Lambiotte has made contributions to the study of random walks on networks.[13] In particular, how random walks can reveal community structure in networks, non-Markovian random walks,[14] and walks on temporal networks.[15]

Brain networks

Lambiotte has made contributions to the study of both the structure and dynamics of brain networks, in particular the modular organisation of the brain.[16][17]

Higher-order networks

Lambiotte has studied the dynamics of higher-order networks, such as multiplex networks, temporal networks and hypergraphs.[18][19]

Social networks

Lambiotte has made contributions to the field of social network analysis including analysis of mobile phone data, online social networks, and signed networks.[20]

References

  1. ^ a b c d "Renaud Lambiotte | Mathematical Institute".
  2. ^ "NetSci – the Network Science Society".
  3. ^ a b "Professional Feature - Renaud Lambiotte".
  4. ^ "Renaud Lambiotte - the Mathematics Genealogy Project".
  5. ^ "Renaud Lambiotte". www.lambiotte.be. Retrieved 2025-08-23.
  6. ^ "Prix Verdickt-Rijdams 2015: Les éditions l'Arbre de Diane pour la collection «La tortue de Zénon»".
  7. ^ Masuda, Naoki and Lambiotte, Renaud (2016). A Guide to Temporal Networks. World Scientific Publishing. ISBN 9781786341167.
  8. ^ Lambiotte, Renaud and Schaub, Michael (2021). Modularity and Dynamics on Complex Networks. Cambridge University Press. ISBN 9781108774116.
  9. ^ Blondel, Vincent D.; Guillaume, Jean-Loup; Lambiotte, Renaud; Lefebvre, Etienne (2008). "Fast unfolding of communities in large networks". Journal of Statistical Mechanics: Theory and Experiment (10) P10008. arXiv:0803.0476. Bibcode:2008JSMTE..10..008B. doi:10.1088/1742-5468/2008/10/P10008.
  10. ^ Blondel, Vincent D.; Guillaume, Jean-Loup; Lambiotte, Renaud (2024). "Fast unfolding of communities in large networks: 15 years later". Journal of Statistical Mechanics: Theory and Experiment (10): 10R001. arXiv:2311.06047. Bibcode:2024JSMTE2024jR001B. doi:10.1088/1742-5468/ad6139.
  11. ^ Lambiotte, Renaud; Delvenne, Jean-Charles; Barahona, Mauricio (2015). "Random Walks, Markov Processes and the Multiscale Modular Organization of Complex Networks". IEEE Transactions on Network Science and Engineering. 1 (2): 76–90. arXiv:0812.1770. doi:10.1109/TNSE.2015.2391998.
  12. ^ Schaub, Michael T.; Delvenne, Jean-Charles; Rosvall, Martin; Lambiotte, Renaud (2017). "The many facets of community detection in complex networks". Applied Network Science. 2 (1) 4. doi:10.1007/s41109-017-0023-6. PMC 6245232. PMID 30533512.
  13. ^ Masuda, Naoki; Porter, Mason A.; Lambiotte, Renaud (22 November 2017). "Random walks and diffusion on networks". Physics Reports. 716–717: 1–58. arXiv:1612.03281. Bibcode:2017PhR...716....1M. doi:10.1016/j.physrep.2017.07.007.
  14. ^ Rosvall, Martin; Esquivel, Alcides V.; Lancichinetti, Andrea; West, Jevin D.; Lambiotte, Renaud (2014). "Memory in network flows and its effects on spreading dynamics and community detection". Nature Communications. 5 (4630) 4630. arXiv:1305.4807. Bibcode:2014NatCo...5.4630R. doi:10.1038/ncomms5630. PMID 25109694.
  15. ^ Delvenne, Jean-Charles; Lambiotte, Renaud; Rocha, Luis E. C. (2015). "Diffusion on networked systems is a question of time or structure". Nature Communications. 6 (7366) 7366. arXiv:1309.4155. Bibcode:2015NatCo...6.7366D. doi:10.1038/ncomms8366. PMID 26054307.
  16. ^ Meunier, David; Lambiotte, Renaud; Bullmore, Edward T (2010). "Modular and hierarchically modular organization of brain networks". Frontiers in Neuroscience. 4 (200): 200. doi:10.3389/fnins.2010.00200. PMC 3000003. PMID 21151783.
  17. ^ Meunier, David; Lambiotte, Renaud; Fornito, Alex; Ersche, Karen D; Bullmore, Edward T (2009). "Hierarchical modularity in human brain functional networks". Frontiers in Neuroinformatics. 3 (571): 37. doi:10.3389/neuro.11.037.2009. PMC 2784301. PMID 19949480.
  18. ^ Lambiotte, Renaud; Rosvall, Martin; Scholtes, Igor (2019). "From networks to optimal higher-order models of complex systems". Nature Physics. 15 (4): 313–320. Bibcode:2019NatPh..15..313L. doi:10.1038/s41567-019-0459-y. PMC 6445364. PMID 30956684.
  19. ^ Neuhäuser, Lukas; Mellor, Andrew; Lambiotte, Renaud (2020). "Multibody interactions and nonlinear consensus dynamics on networked systems". Physical Review E. 101 (3) 032310. arXiv:1910.09226. Bibcode:2020PhRvE.101c2310N. doi:10.1103/PhysRevE.101.032310. PMID 32289906.
  20. ^ Szell, Michael; Lambiotte, Renaud; Thurner, Stefan (2010). "Multirelational organization of large-scale social networks in an online world". Proceedings of the National Academy of Sciences. 107 (31): 13636–13641. arXiv:1003.5137. Bibcode:2010PNAS..10713636S. doi:10.1073/pnas.1004008107. PMC 2922277. PMID 20643965.
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