# Modeling complex systems with adaptive networks

arXiv: https://arxiv.org/abs/1301.2561 · HTML: https://mlsanity.com/paper/1301.2561/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2013.

## Built on by (most cited first, 5 of 5)

- [A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2209.03859/) (2022)
- [Toward Data Systems That Are Business Semantic Centric and AI Agents Assisted](https://mlsanity.com/paper/2506.05520/) (2025)
- [Scaling up the self-optimization model by means of on-the-fly computation of weights](https://mlsanity.com/paper/2211.01698/) (2022)
- [Decreasing the Computing Time of Bayesian Optimization using Generalizable Memory Pruning](https://mlsanity.com/paper/2309.04510/) (2023)
- [Autonomous Business System via Neuro-symbolic AI](https://mlsanity.com/paper/2601.15599/) (2026)

## Abstract (arXiv)

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.