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DYSONET summary

The project aims to further develop the basic science of complex social networks in a quantitative way, and apply our findings to the dynamics of human behavior. We intend to generalize and apply our results to a wider range of networks, including economic, traffic, and environmental networks, thus establishing an interdisciplinary program of research based in complex social networks.

Thus, the methods and techniques to be developed, based on Statistical Physics concepts, will be of further use in a variety of other fields. To date, the application of complexity to the topology and emergent dynamic behavior in social networks has been mostly limited, qualitative, and rather empirical. We propose to use quantitative characterization of complex social networks by analyzing a number of real-world phenomena, including: crowd behavior, search strategies, traffic flow, dynamics of human relationship networks, spread of epidemics, as well as dynamic patterns in other disciplines, such as Economics and Finance, and Environmental networks.

A principal goal is to characterize, optimize and control the structure, dynamics and flow in complex social networks, aiming to uncover the organizing principles that limit and influence the emergence of collective behavior in such systems. This work will also test the hypothesis that the dynamics of certain collective human behavior is governed by an underlying drive to optimize certain aspects of the underlying network on which this behavior takes place. Our studies will use extensive real-world data of social nature, which will be collected in the frame of the project.

The consortium will bring together scientists of various disciplines working in the emerging field of complex networks, in order to study topology and dynamics of social networks at a large scale. Our study will enable to develop useful applications, e.g. approaches for efficient optimisation, to control panic and optimize searching of lost people, etc.


(c) Computational Physics Group A.U.Th.