Status:
Professor of Networks and Nonlinear Systems
Personal website:
+44 1865 280608
ORCID iD:
https://orcid.org/0000-0002-0583-4595
Research groups:
Address
University of Oxford
Andrew Wiles Building
Radcliffe Observatory Quarter
Woodstock Road
Oxford
OX2 6GG
Recent Books:
Complex Networks and Their Applications VII
ISBN-13: 9783030054106
(1 January 2019)
A Guide to Temporal Networks
ISBN-13: 9781786341167
(28 July 2016)
Modularity and dynamics on complex networks
Recent Publications:
Modeling collective anticipation and response on Wikipedia
Proceedings of the International AAAI Conference on Web and Social Media (ICWSM)
issue 2021
volume 15
page 315-326
(22 May 2021)
Random walks and community detection in hypergraphs
Journal of Physics: Complexity
volume abs/2010.14355
(12 April 2021)
Non-linear network dynamics with consensus-dissensus bifurcation
Journal of Nonlinear Science
(15 January 2021)
Extracting Complements and Substitutes from Sales Data: A Network Perspective
CoRR
volume abs/2103.02042
(1 January 2021)
Modelling Non-Linear Consensus Dynamics on Hypergraphs
Journal of Physics: Complexity
volume abs/2007.09391
(1 December 2020)
Opinion Dynamics with Multi-Body Interactions
Proceedings of NETGCOOP 2020
(1 July 2020)
Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle
Science Advances
(5 June 2020)
Full text available
Research interests:
Complex systems - Dynamics on networks - Temporal networks
Preferred address:
Mathematical Institute, Andrew Wiles Building, University of Oxford, Radcliffe Observatory Quarter, Woodstock Rd, Oxford OX2 6GG, UK
Further details:
Renaud Lambiotte has a PhD in physics from the Université libre de Bruxelles. After postdocs at ENS Lyon, Université de Liège, UCLouvain and Imperial College London, and a professorship in Mathematics at the University of Namur, he is currently associate professor at the Mathematical Institute of Oxford University. His main research interests are the modelling and analysis of processes taking place on large networks, with a particular focus on social and brain networks.