NetSE: Small: Structure and Dynamics of Complex Networks

University Of Southern California, Los Angeles CA

Investigators

Abstract

The Social Web, or Web 2.0, has changed the way people connect with each other and use information. Sites such as Twitter, Flickr, and Digg allow people to create content, annotate it with descriptive labels, and befriend others to create communities. The collective knowledge and expertise of the community is expressed through the links between people and information. The key to extracting this knowledge is understanding the structure and dynamics of networks. This project will study dynamics of information spread on networks and how it relates to network structure and quality of information. In previous work, investigator has developed a mathematical framework to study the properties of static networks. She showed that a centrality metric based on the number of paths connecting nodes can be used to identify groups and important nodes within them. However, looking at static structure ignores valuable temporal information that can be used to improve the ability to identify influential nodes and hidden groups, as well as quickly and reliably predict important trends and evaluate the quality of information. The investigator will extend these metrics to dynamic networks and model information spread on networks. The investigator will apply these methods to complex networks that link different types of entities, namely, people, content, and groups. Considering network structure in the dynamics of information spread will lead to more effective tools to leverage community's knowledge to address a number of problems, including identifying important trends, assessing information quality, and separating true information from rumors.