Integrating methods from network analysis, natural language processing, and machine learning with theories from the social sciences to advance knowledge and discovery about interaction-based and communication-based social systems

Information Networks

Grad course, taught in Spring 2012-2013 at UIUC, iSchool by Jana Diesner

We meet Tuesdays 1pm to 3.50 pm

 

How does information emerge, spread and vanish in society and online? Who is talking to whom about what, and with what sentiment? What do the structure, functioning and dynamics of information networks imply for constructing knowledge resource, managing collaborative work processes and fostering innovation? Learn how to answer these questions in 590IN.
Network Analysis in general has become a widely adopted approach for studying the interactions between social agents, information and infrastructures. The strong demand for solid expertise and skills in network analysis has been fueled by the widespread acknowledgement that everything is connected, the popularity of interlinked information networks, and advances in computational solutions for collecting, visualizing and analyzing network data.
This interdisciplinary course introduces students to fundamental theories, concepts, methods and applications for information network analysis. Students learn how to approach information network analysis in an informed, systematic and analytically rigorous fashion. At the end of the course, students will be able to design, manage and execute network analysis projects for scholarly and commercial use, and to critically assess information network studies. 


No prerequisites, no programming skills required.  
Credit: 4