Inferring dynamic topology for decoding spatiotemporal structures in complex heterogeneous networks
Abstract
Inferring connections forms a critical step toward understanding large and diverse complex networks. To date, reliable and efficient methods for the reconstruction of network topology from measurement data remain a challenge due to the high complexity and nonlinearity of the system dynamics. These obstacles also form a bottleneck for analyzing and controlling the dynamic structures (e.g., synchrony) and collective behavior in such complex networks. The novel contribution of this work is to develop a unified data-driven approach to reliably and efficiently reveal the dynamic topology of complex networks in different scales—from cells to societies. The developed technique provides guidelines for the refinement of experimental designs toward a comprehensive understanding of complex heterogeneous networks.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 27, 2018
- Source ID
- 10.1073/pnas.1721286115
Entities
People
- Daniel Granados-fuentes
- Erik D. Herzog
- Guy Bloch
- István Z. Kiss
- Jr-Shin Li
- Liang Wang
- Michael Sebek
- Shuo Wang
- William J. Schwartz
Organizations
- Air Force Office of Scientific Research
- Hebrew University of Jerusalem
- National Institutes of Health
- National Science Foundation
- Saint Louis University
- Washington University in St. Louis