Self-Organization in Networks: A Data-Driven Koopman Approach
Optimization and Control
2017-09-28 v1 Adaptation and Self-Organizing Systems
Physics and Society
Abstract
Networks out of equilibrium display dynamics characterized by multiple equilibria and sudden transitions. These transitions arise when each node leaves its natural stable state and joins to an organized global activation behavior. In this paper, we study local patterns near regime shifts in Network Synchronization and Self-Organized Criticality (SOC) by a Koopman spectrum data-driven approach. To illustrate these ideas, we use synchronized behavior from an Integral-and-Fire oscillators (IFO) system and SOC in the Bak-Sneppen model.
Keywords
Cite
@article{arxiv.1709.09576,
title = {Self-Organization in Networks: A Data-Driven Koopman Approach},
author = {Claudia Caro-Ruiz and Duvan Tellez-Castro and Andres Pavas and Eduardo Mojica-Nava},
journal= {arXiv preprint arXiv:1709.09576},
year = {2017}
}
Comments
paper accepted in CCAC 2017