"Network Geometry", a càrrec de la professora M. Ángeles Serrano (UB-ICREA)

M. Ángeles Serrano obtained her Ph.D. in Physics at the Universitat de Barcelona (UB) in 1999 with a thesis about gravitational wave detection. In 2000, she also received her Masters in Mathematics for Finance at the CRM-Universitat Autònoma de Barcelona. After four years in the private sector as IT consultant and mutual funds manager, Prof. Serrano returned to academia in 2004 to work in the field of Network Science. Subsequently, she was a researcher at Indiana University (USA), the École Polytechnique Fédérale de Lausanne (Switzerland), IFISC Institute (Spain), and held a Ramón y Cajal research associate appointment at UB until october 2015. The results of her investigations are summarized in major peer reviewed international scientific journals -including Nature, PNAS, PRL, ...-, book chapters, and conference proceedings. Prof. Serrano leads and participates in several research projects at the international and national levels. She is also actively involved in advising and research supervision. She serves in evaluation panels and program scientific committees, and acts as a reviewer in several international journals. In February 2009, she obtained the Outstanding Referee award of the American Physical Society. She is a Founder Member of Complexitat, the Catalan Network for the study of Complex Systems, and a Promoter Member of UBICS, the Universitat de Barcelona Institute of Complex Systems.

Many of these fundamental properties are well explained by a family of hidden metric space network models that led to the discovery that the latent geometry of many real networks is hyperbolic. Hyperbolicity emerges as a result of the combination of popularity and similarity dimensions into an effective distance between nodes, such that more popular and similar nodes have more chance to interact. The geometric approach permits the production of truly cartographic maps of real networks that are not only visually appealing, but enable applications like efficient navigation and the detection of communities of similar nodes. Recently, it has also enabled the introduction of a geometric renormalization group that unravels the multiple length scales coexisting in complex networks, strongly intertwined due to their small world property.