• data mining
• data science
• human mobility
• network science
• social networks

Understanding human social behavior is a longstanding dream of mankind, a really profound point from both pragmatic and philosophical perspectives. The ability of drawing a comprehensive picture of human behavior and dynamics is helpful in many problems, which characterize our modern and complex society: the prevention of devastating pandemic diseases; the diffusion of new ideas or technologies over a social network; the patterns of success in different spheres of our activities. Big Data are nowadays a powerful social microscope which paves the road to realize the dream, allowing to "photograph" the main aspects of the society and to create a comprehensive picture of human behavior.

This thesis proposes to study human behavior and dynamics through a combination of techniques from network science and data mining. In the context of human mobility, we use mobile phone data and GPS trajectories from vehicles to show that people can be profiled into two distinct categories, namely returners and explorers, according to their recurrent mobility patterns. We construct a new mobility model that can reproduce the observed dichotomy and show that returners and explorers play a distinct quantifiable role in spreading phenomena. We also investigate the issue of activity recognition from human movements by presenting a classification model to recognize the activity performed by an individual by observing some characteristics of her movements.

We then move from individuals to connections, entering the domain of social network analysis. We investigate the challenging problem of community detection in dynamic social networks presenting Tiles, an innovative algorithm able to track the history of social communities in a streaming fashion. We also address the fascinating problem of the information diffusion over a social network, studying the spreading of musical tastes over a music social media. We show that certain individuals act as musical leader or innovators and that they can generate three different patterns of diffusion.

Finally, we investigate the potentiality of Big Data in providing estimate for the socio-economic development of a territory. We use mobile phone data and GPS trajectories from vehicles to show that human mobility, and mobility diversity in particular, is highly correlated to wellbeing at municipality and province level. Individuals' movements and quality of life are linked aspects of society, opening the scenario for the definition of new statistical index that rely on Big Data to monitor the economic health of a territory.

We conclude the thesis by revising the most promising research directions which open up from the results summarized in the thesis and introducing other interesting aspects related to the data-driven study of human behavior and dynamics.