Date of Degree

9-2018

Document Type

Dissertation

Degree Name

Ph.D.

Program

Physics

Advisor

Hernan Makse

Committee Members

Lucas Parra

Tobias Schaefer

Jeffrey Morris

Flaviano Morone

Subject Categories

Physics | Statistical, Nonlinear, and Soft Matter Physics

Keywords

Complex networks, k-core algorithm, thermodynamics, maximum entropy, random networkstheory, networks

Abstract

The study of complex networks is, at its core, an exploration of the mechanisms that control the world in which we live at every scale, from particles no bigger than a grain of sand and amino acids that comprise proteins, to social networks, ecosystems, and even countries. Indeed, we find that, regardless of the physical size of the network's components, we may apply principles of complex network theory, thermodynamics, and statistical mechanics to not only better understand these specific networks, but to formulate theories which may be applied to problems on a more general level. This thesis explores several networks at vastly different scales, ranging from the microscopic (amino acids and frictional packed particles) to the macroscopic (human subjects asked to view a set of videos) to the massive (real ecosystems and the "financial ecosystem" (Haldane 2011, May 2008) of stocks in the S&P500 stock index). The networks are discussed in chronological order of analysis. We begin with a review of k-core theory, including its applications to certain dynamical systems, as this is an important concept to understand for the next two sections. A discussion of the network structure (specifically, a k-shell decomposition) of both ecological and financial dynamic networks, and the implications of this structure for determining a network's tipping point of collapse, follows. Third, this same k-shell structure is examined for networks of frictional particles approaching a jamming transition, where it is seen that the jamming transition is a k-core transition given by random network theory. Lastly comes a thermodynamical examination of human eye-tracking networks built from data of subjects asked to watch the commercials of the 2014 Super Bowl Game; we determine, using a Maximum Entropy approach, that the collective behavior of this small sample can be used to predict population-wide preferences. The behavior of all of these networks are explained using aspects of network theoretical and statistical mechanics frameworks and can be extended beyond the specific networks analyzed herein.

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