  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.
