Data Analysis for Complex Systems: A Linear Algebra Approach

By Scott Pauls, Dan Rockmore, Greg Leibon

The analysis of complex systems--from financial markets and voting patterns to ecosystems and food webs--can be daunting for newcomers to the subject, in part because existing methods often require expertise across multiple disciplines. This book shows how a single technique--the partition decoupling method--can serve as a useful first step for modeling and analyzing complex systems data. Accessible to a broad range of backgrounds and widely applicable to complex systems represented as high-dimensional or network data, this powerful methodology draws on core concepts in network modeling and analysis, cluster analysis, and a range of techniques for dimension reduction. The book explains these and other essential concepts and provides several real-world examples to illustrate how a data-driven approach can illuminate complex systems.

Provides a comprehensive introduction to modeling and analysis of complex systems with minimal mathematical prerequisites
Focuses on a single technique, thereby providing an easy entry point to the subject
Explains analytic techniques using actual data from the social sciences
Uses only linear algebra to model and analyze large data sets
Includes problems and real-world examples
An ideal textbook for students and invaluable resource for researchers with a wide range of backgrounds and preparation
Proven in the classroom

• Format: Paperback
• Language: English
• ISBN: 0691139180
• ISBN13: 9780691139180
• Release Date: December 2040
• Publisher: Princeton University Press
• Length: 168 Pages