Our papers are the official record of our discoveries. They allow others to build on and apply our work. Each paper is the result of many months of research, so we make a special effort to make them clear, beautiful and inspirational, and publish them in leading journals.
Naturally occurring networks have an underlying scale-free structure that is often clouded by finite-size effects in the sample data.
A mathematical model captures the temporal and steady state behaviour of networks whose two sets of nodes either generate or destroy links.
Statistical physics harnesses links between maximum entropy and information theory to capture null model and real-world network features.
Bipartite networks model the structures of ecological and economic real-world systems, enabling hypothesis testing and crisis forecasting.
An adaptive network of oscillators in fragmented and incoherent states can re-organise itself into connected and synchronized states.
Information theory fixes weighted networks’ degeneracy issues with a generalisation of binary graphs and an optimal scale of link intensities.