Tightly knit : spreading processes in empirical temporal networks

Abstract: We live in a tightly knit world. Our emotions, desires, perceptions and decisions are interlinked in our interactions with others. We are constantly influencing our surroundings and being influenced by others. In this thesis, we unfold some aspects of social and economical interactions by studying empirical datasets. We project these interactions into a network representation to gain insights on how socio-economic systems form and function and how they change over time. Specifically, this thesis is centered on four main questions: How do the means of communication shape our social network structures? How can we uncover the underlying network of interests from massive observational data? How does a crisis spread in a real financial network? How do the dynamics of interaction influence spreading processes in networks? We use a variety of methods from physics, psychology, sociology, and economics as well as computational, mathematical and statistical analysis to address these questions.