Bio

Ginestra Bianconi is Professor of Applied Mathematics in the School of Mathematical Sciences of Queen Mary University of London and a Turing Fellow at The Alan Turing Institute. Currently she is Chief Editor of JPhys Complexity, Editor of PloSOne, and Scientific Reports, and Associate Editor of Chaos, Solitons and Fractals.

Her research activity on statistical mechanics and network science includes network theory and its interdisciplinary applications. She has formulated the Bianconi-Barabasi model that displays the Bose-Einstein condensation in complex networks. She has worked in network entropy and network ensembles and on dynamical processes on networks. In the last years she has been focusing on multilayer networks, network geometry and topology, percolation and network control. Ginestra is the author of the book 'Multilayer Networks: Structure and Function' by Oxford University Press.

Research interests

Ginestra Bianconi works on applied mathematics and network science. Her research covers different aspects of the field including the statistical modeling of networks and the formulation of advanced information theory methods to extract relevant information from network data. Her major contributions include works that mathematically model complex systems and  predict their functional behaviour. Recently her research focus is on on generalised network structures including multilayer networks and  simplicial complexes.

Ginestra's research is having impact in different disciplines ranging from neuroscience to communication networks. Her main achievements and the impact on different disciplines are:

  • Multilayer networks
    Ginestra is an expert on the recent field of multilayer networks contributing on determining modelling frameworks currently used across different disciplines from neuroscience to financial networks. She has also worked on centrality measures and controllability of multilayer networks.
  • Robustness of networks and percolation theory
    Ginestra has made important contributions in percolation theory of multilayer networks characterising the robustness of infrastructure and transportation networks. 
  • Network topology and geometry
    Ginestra has formulated maximum entropy models of simplicial complexes and proposed a mathematical framework called "Network geometry with flavor" for emergent network geometry. She has revealed the effect that network geometry has on dynamics focusing in particular on percolation, synchronization and Gaussian processes.
  • Network topology and neuroscience
    Ginestra's work in collaboration with neuroscientists has unveiled the scale-free structure of functional brain networks and the interplay between topology and dynamics in neuronal cultures.
  • Inference
    Ginestra has developed an information theory of networks that has allowed the developments of entropy based measures to extract information from social, biological and transportation networks.
  • Communication networks
    The Bianconi-Barabasi model and the Bose-Einstein condensation in complex networks has been widely used to study the internet.