The lack of predictive power over complex systems either designed by humans or evolved by nature, is a foundational problem in contemporary science. Real networks such as the Internet, the Web, social and biological networks, have acquired emergent large-scale properties that are beyond our full understanding, much less prediction or control. Astonishingly, these emergent properties are the same across networks from drastically different domains, and are the ones required to facilitate the optimality of some important network functions, such as information transport.

In the past several years we have been developing a powerful and unique geometric theory explaining the ubiquitous common structure of complex networks, and its relation to the optimality of information transport. In this theory, network nodes are mapped to points in hyperbolic spaces, which lie hidden beneath the observable topologies. These spaces are called “hidden”, as they play the role of an underlying coordinate system, not readily observable by examining the network topology. Nodes closer in the underlying space are connected in the observable network topology with higher probability.

In this talk, we will first review this geometric network theory, and then discuss statistical inference methods for inferring the hyperbolic geometry of real networks. We will discuss applications of our results in the following contexts: (i) in routing/navigation in the Internet and other complex networks; (ii) in predicting missing or future links in real networks; and (iii) in network community detection. We will further give an overview of our latest research results on the geometric organization of real multilayer/multiplex systems. We will conclude the talk with open problems and future research directions.

About Fragkiskos Papadopoulos

Fragkiskos Papadopoulos is an Assistant Professor of the Department of Electrical Engineering, Computer Engineering and Informatics at Cyprus University of Technology. He received the Diploma in Electrical and Computer Engineering from the National Technical University of Athens in 2002. In 2004 and 2007 he received respectively the MSc and PhD degrees in Electrical Engineering from the University of Southern California. During 2007-2009 he was a postdoctoral researcher at the Center for Applied Internet Data Analysis at the University of California, San Diego. His research interests are in network theory and network geometry. Particularly, his interests include: geometric approaches to the analysis and prediction of real networks; navigation/routing in complex networks; statistical inference and network mapping; geometry and correlations in real multiplex networks; network performance; and large-scale simulation.

Este evento se impartirá en inglés