Modeling complex systems with adaptive networks

TL;DR

This paper reviews the fundamental concepts of adaptive networks, highlighting their coevolving topologies and states, and discusses recent applications in real-world systems like search and rescue, rule discovery, and corporate mergers.

Contribution

It introduces key properties of adaptive networks and presents recent computational modeling applications to practical complex systems.

Findings

Adaptive networks exhibit coevolving topologies and states.

Applications include search and rescue network development, rule discovery, and cultural integration.

The paper highlights the versatility of adaptive network modeling in real-world problems.

Abstract

Adaptive networks are a novel class of dynamical networks whose topologies and states coevolve. Many real-world complex systems can be modeled as adaptive networks, including social networks, transportation networks, neural networks and biological networks. In this paper, we introduce fundamental concepts and unique properties of adaptive networks through a brief, non-comprehensive review of recent literature on mathematical/computational modeling and analysis of such networks. We also report our recent work on several applications of computational adaptive network modeling and analysis to real-world problems, including temporal development of search and rescue operational networks, automated rule discovery from empirical network evolution data, and cultural integration in corporate merger.