Network inference: Predicting with networks and statistical physics

Almost 20 years after the publication of the seminal papers in network science, we have become quite proficient at characterizing complex networks. Only recently, however, have we started to investigate whether network theory can be used to make concrete, testable predictions about the world, beyond mere characterization. For example, suppose someone gives you a network representing harmful interactions between pairs of drugs, and asks you "Which interactions in the network are likely to be wrong, and which are likely to be missing?" I will discuss how to answer such questions using non-parametric Bayesian methods, statistical physics approaches, and simple generative network models which make very mild assumptions about network structure. The talk will show applications to problems as diverse as the prediction of conflict within work teams, the prediction of user ratings on movies and books, and protein-protein interaction networks. I will also discuss how can we use these approaches to better understand network structure and network processes in general.

Tsuyoshi Murata

Tokyo Institute of Technology

David Rapin


Roger Guimera

Universitat Rovira i Virgili.



Bio. :  Roger Guimerà (Barcelona, 1976) is an ICREA Research Professor at Universitat Rovira i Virgili, in Tarragona, Catalonia. He graduated in Physics at Universitat de Barcelona in 1998, and obtained a PhD in Chemical Engineering from Universitat Rovira i Virgili in 2003. He then moved to Northwestern University where he worked as a postdoctoral fellow and later as a Fulbright Scholar. In 2008 he became a Research Assistant Professor at Northwestern's Department of Chemical and Biological Engineering, before accepting his current position at ICREA in 2010. Roger's research is devoted to the study of complex systems and, particularly, of the structure of complex networks and the interplay between network structure and dynamics. During his career, he has: (i) made methodological contributions to the study of complex networks, and (ii) used complex network analysis to gain understanding on a number of systems. He has been awarded the Premi Nacional de Recerca al Talent Jove (2010), the Erdös-Rényi Prize in Network Science (2012), and the Young Scientist Award for Socio- and Econophysics (2014).