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Dr Petra Vértes

Research Interests

Networks & Complexity

Complex networks are the central theme tying together most of my research activities to date.

I initially became interested in complex networks (and complexity more generally) in the final year of my undergraduate studies in theoretical physics, during which I developed an algorithm for the spatial embedding of networks. What drew me especially to networks is their applicability to a wide range of systems. Indeed, a great variety of phenomena, both natural and man-made, can be viewed and modeled as networks. Our brain is a network of neurons, each of which relies on a complex network of chemical interactions between its constituent molecules. The World Wide Web is a network of hyperlinks between sites, our friendships constitute social networks, we travel on public transportation networks and catch diseases that often spread on a network of person-to-person contact.

Although I have since specialized in the study of brain networks (at the micro and macro scales), I maintain a strong interest in broader questions about dynamical processes on networks, as well as the dynamics of time-evolving networks and, most recently, questions of network visualization.

I also strongly believe that there is much to be gained from maintaining strong links between researchers applying the tools of network science in a variety of disciplines. It is in this spirit that I co-founded the Cambridge Networks Network.

I have recently become interested in Immunology and I am looking to collaborate with immunologists in Cambridge, analyzing immunological data from a networks perspective.


complex systems ; networks ; brain


  • schizophrenia

Key Publications

Vértes PE, Alexander-Bloch A, Gogtay N, Giedd J, Rapoport J, and Bullmore ET Simple models of human brain functional networks. Proc. Natl. Acad. Sci. (USA), 2012, 109(15): 5868-73.

Alexander-Bloch A, Vértes PE, Stidd R, Lalonde F, Clasen L, Rapoport J, Giedd J, Bullmore ET, Gogtay N The anatomical distance of functional connections predicts brain network topology in health and schizophrenia. Cereb Cortex, 2012, doi: 10.1093/cercor/bhr388.