Data Analysis for Complex Systems: A Linear Algebra Approach

by Greg Leibon, Scott Pauls, Dan Rockmore

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.

168 pages, Paperback

Expected publication December 25, 2040