Research in the Centre for Networks and Collective Behaviour

Members of CNCB conduct research into a variety of topics connected to network science and emergent phenomena.

A mathematician's blackboard
Wave-front analysis in a model of growing bacterial colonies

Some examples of topics explored within the centre are:

Eco-evolutionary dynamics

Ecology and evolution are collective dynamical processes that have shaped the world around us over millennia. Theodosius Dobzhansky famously said that "nothing in biology makes sense, except in the light of evolution", but the course of evolution can be difficult to understand and predict. Mathematical models of the collective dynamics of individuals and whole populations can shed light on these complex processes. In recent work, CNCB members George Constable and Tim Rogers have employed time-scale separation methods to reveal a surprising route to the evolution of altruistic behaviour.

Animal social networks

We now know that we can learn a lot about many animal populations by analysing their social networks, but we also know that we need more and better models of network formation and evolution before we can do more. CNCB Director Dick James collaborates with field biologists to investigate the structure and dynamics of animal social networks. A recent highlight of this work has been the discovery of species-wide tool use in the Hawaiian crow [Rutz, C., Klump, B., Komarczyk, L., Leighton, R., Kramer, J., Wischnewski, S., Sugasawa, S., Morrissey, M., James, R., St Clair, J., Switzer, R. and Masuda, B. Nature 537, 403-7 (2016)].

Network structure analysis

Tiago Peixoto's research focuses on characterizing, identifying and explaining large-scale patterns found in the structure and function of complex network systems — representing diverse phenomena with physical, biological, technological, or social origins — using principled approaches from statistical physics, nonlinear dynamics and Bayesian inference.

Random matrix theory

The structure of network interactions are commonly encoded in high-dimensional matrices, whose behavioural characteristics are captured by their eigenvalue spectra. One strand of Tim Rogers's research is into random matrix theory applied to networks.