Network science is an academic and applied field that studies complex networks in domains such as telecommunications, computer networks, biological, cognitive and semantic networks, and social networks. The field draws on theories and methods from various fields, including graph theory (from mathematics), statistical mechanics (from physics), information spread and diffusion (from both epidemiology and communications), and social structure (from sociology), to only name a few. Recently, because of the broad availability of datasets in highly applied domains like social media, corporate filings and transportation, network science and research areas like AI and Big Data have started to converge in exciting ways, leading to interdisciplinary collaborations and scientific verification (or debunking) of theories previously believed to be quantitatively untestable.
AI, Networks and Society is a collection of projects in our group that seeks to use various sources of data to learn more about society using empirically rigorous, data-driven methodologies. Datasets used in our investigations include social media data (primarily, Twitter), news data, structured data (such as tables and graphs), economic data and election data.