Symmetries in the time-averaged dynamics of networks: Reducing unnecessary complexity through minimal network models
Abstract
Complex networks are the subject of fundamental interest from the scientific community at large. Several metrics have been introduced to characterize the structure of these networks, such as the degree distribution, degree correlation, path length, clustering coefficient, centrality measures, etc. Another important feature is the presence of network symmetries. In particular, the effect of these symmetries has been studied in the context of network synchronization, where they have been used to predict the emergence and stability of cluster synchronous states. Here, we provide theoretical, numerical, and experimental evidence that network symmetries play a role in a substantially broader class of dynamical models on networks, including epidemics, game theory, communication, and coupled excitable systems; namely, we see that in all these models, nodes that are related by a symmetry relation show the same time-averaged dynamical properties. This discovery leads us to propose reduction techniques for exact, yet minimal, simulation of complex networks dynamics, which we show are effective in order to optimize the use of computational resources, such as computation time and memory.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 01, 2019
- Source ID
- 10.1063/1.5081023
Entities
People
- Abu Bakar Siddique
- Francesco Sorrentino
- Louis M Pecora
Organizations
- Defense Threat Reduction Agency
- Division of Civil, Mechanical & Manufacturing Innovation
- National Science Foundation Directorate for Engineering
- Office of Naval Research
- United States Naval Research Laboratory
- University of New Mexico