Statistical mechanics of temporal and interacting networks
Permanent URL:
http://hdl.handle.net/2047/d20003046
Vespignani, Alessandro (Committee member)
Barabási, Albert-László (Committee member)
Stepanyants, Armen (Committee member)
Recently, temporal networks and interacting networks have attracted large interest. Indeed many networks are interacting or formed by a multilayer structure. Example of these networks are found in social networks where an individual might be at the same time part of different social networks, in economic and financial networks, in physiology or in infrastructure systems. Moreover, many networks are temporal, i.e. the links appear and disappear on the fast time scale. Examples of these networks are social networks of contacts such as face-to-face interactions or mobile-phone communication, the time-dependent correlations in the brain activity and etc. Understanding the evolution of temporal and multilayer networks and characterizing critical phenomena in these systems is crucial if we want to describe, predict and control the dynamics of complex system.
In this thesis, we investigate several statistical mechanics models of temporal and interacting networks, to shed light on the dynamics of this new generation of complex networks. First, we investigate a model of temporal social networks aimed at characterizing human social interactions such as face-to-face interactions and phone-call communication. Indeed thanks to the availability of data on these interactions, we are now in the position to compare the proposed model to the real data finding good agreement.
Second, we investigate the entropy of temporal networks and growing networks to provide a new framework to quantify the information encoded in these networks and to answer a fundamental problem in network science: how complex are temporal and growing networks.
Finally, we consider two examples of critical phenomena in interacting networks. In particular , on one side we investigate the percolation of interacting networks by introducing antagonistic interactions. On the other side, we investigate a model of political election based on the percolation of antagonistic networks . The aim of this research is to show how antagonistic interactions change the physics of critical phenomena on interacting networks.
We believe that the work presented in these thesis offers the possibility to appreciate the large variability of problems that can be addressed in the new framework of temporal and interacting networks.
Statistical Mechanics
OS and Networks
Physics
Statistical Models
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