Spectral Models of Security: New Methods for Detecting Evolving Community Structure in Societal-Scale Data
Abstract
The rapid proliferation of mobile phones and other digital devices provides immense opportunities to observe and understand the rapidly changing structure of communities in developing and conflict-affected states. However, current state-of-the-art computational methods used to analyze such data are notoriously ill-suited to answer basic, fundamental questions in the social science and policy arena. While many new, provably efficient algorithms for community detection have been recently developed, these methods have several key limitations: they rarely scale to real-world datasets consisting of millions of interconnected actors; they are not applicable to dynamic contexts where network structure evolves over time; and they are almost never validated against ground-truth data from reliable, independent sources.In this project, the performer will develop new, scalable methods for modeling and studying evolving, societal-scale communities and social networks. The technical focus will be on adapting spectral machine learning methods to real-world contexts with dynamic social data. This requires innovation to make existing algorithms more scalable, better suited to hypothesis testing, and appropriate to non-stationary regimes, where community structure changes continuously over time. The practical application of this method will be to study the impact of significant geopolitical events on the social fabric of micro- and meso-scale community networks in multiple developing countries.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 03, 2017
- Source ID
- N000141712313
Entities
People
- Joshua Evan Blumenstock
Organizations
- Office of Naval Research
- United States Navy
- University of California Regents