30 March 2012, Computational Social Choice Seminar, Fosca Giannotti
Complex network analysis is a field characterized by a growing interest among scientists in the last decade. This attention may be explained by the many challenges posed by the analysis of complex systems, and by the fact that these complex systems are ubiquitous in nature and society. However, the main effort has been mainly devoted in studying statistical properties and behavior of actors in a single relationship model. It is a matter of common experience that this is not reflecting how society and nature works in reality: complex systems are characterized by competing forces, dynamics that express themselves beyond what a simple graph can represent. In this setting, traditional complex network analysis is not enough. There is the need for understanding a multi-faceted, or multidimensional, reality. We propose a framework for Multidimensional Network Analysis, to extend the known metaphors of complex network analysis and grasp the complexity of real world phenomena. The emergency and need for a multidimensional network analysis is here presented, along with an empirical proof of the ubiquity of this multifaceted reality in different complex networks. Then, we tackle the foundations of the multidimensional setting at different levels, both by looking at the basic extensions of the known model and by developing novel algorithms and frameworks for well-understood problems, such as community discovery, temporal analysis, link prediction and more. We finally present a network-theoretic economic scenario, both at a macro-level (the analysis of international trade based on the country-product network) and a micro-level (the analysis of shopping behavior based on the customer-product network).