Modeling complex systems with adaptive networks

2013/01/03 by Hiroki Sayama, Irène Pestov, Irene Pestov +7 · 195 citations
Computer Science · Economics, Econometrics and Finance · Physics and Astronomy · #Artificial intelligence #Artificial neural network #Class (philosophy) #Complex Network Analysis Techniques #Complex Systems and Time Series Analysis #Complex adaptive system #Complex network #Complex system #Computer network #Computer science #Data science #Distributed computing #Dynamical systems theory #Network topology #Opinion Dynamics and Social Influence #Theoretical computer science #cs.SI #nlin.AO #physics.soc-ph

paper · pdf · doi:10.1016/j.camwa.2012.12.005

published in Computers & Mathematics with Applications 65(10), 1645-1664 (Elsevier BV) · 24 pages, 11 figures, 3 tables

openalex publication_date 2013/01/03 · arxiv created 2013/01/11 · arxiv updated 2017/05/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

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.

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