Algorithms for Social Networks

Carleton College, Northfield MN

Investigators

Abstract

As more and more day-to-day communication moves online, there is newfound access to large-scale data on human interactions and friendships through the records of activity in online communities. Social networks, structures encoding these interactions, contain large amounts of information about people and the social fabric that connects them. The study of these networks is an emerging interdisciplinary topic, found at the intersection of computer science with sociology, economics, and other disciplines. This research seeks to develop basic understanding of social networks and their applications, such as marketing, epidemiology, and the search for information and expertise through trusted connections. The investigator is involving undergraduate students in the research program and is also developing educational materials based on social networks for use throughout the computer science curriculum. This research involves a broad array of questions related to algorithmic aspects of social networks, with two main threads. One thread is the development and study of formal mathematical models of social networks, especially focusing on models in which salient features of real-world social networks are proveably reproduced. The second thread is the systematic analysis of large-scale real-world social networks, especially focusing on the possibility of extracting useful predictive information from those networks. The investigator is simultaneously pursuing an educational plan to integrate social networks into the computer science curriculum at Carleton College, a selective undergraduate-only liberal arts college, through the development of courses and course materials integrating complex real-world phenomena into the computer science curriculum at all levels.