NetSE: Small: Complex Adaptive Networks: Generative Models and Statistical Analysis

我们的社会越来越依赖各种技术网络，如交通系统、电力配电网、计算机网络等。随着这些网络的演变和复杂性的增长，它们的动态行为变得越来越难以理解和预测。这个项目将基于相互作用的随机过程的概念，开发一个通用框架，用于模拟这种复杂的自适应网络的增长和演化。这种方法背后的直觉是，形成网络的相互作用由随机过程的集体状态通知，而这些过程本身受到形成的网络结构的影响。这种反馈机制的存在对于捕捉许多真实世界网络的真实行为至关重要。这项研究将发展严格的数学方法来分析自适应网络的结构和动力学特性，并定义新的信息论方法来量化它们的复杂性。广泛的影响：我们国家？S未来的技术基础设施将取决于我们控制大规模、动态的互联异质实体网络的能力。这项工作将有助于更好地理解、描述和预测此类网络的集体行为。该项目将在一个重要的跨学科领域培训新的专业人员和科学家，并编写关于复杂自适应网络的研究生课程材料。

Our society is becoming increasingly dependent on various technological networks, such as transportation systems, electrical power?distribution grids, computer networks, and so on. As those networks evolve and grow in complexity, their dynamical behavior is becoming difficult to understand and predict. This project will develop a general framework for modeling growth and evolution of such complex adaptive networks, based on the notion of interacting stochastic processes. The intuition behind this approach is that the interactions that form the network are informed by the collective state of the stochastic processes, while those processes themselves are affected by the forming network structure. The presence of such a feedback mechanism is vital for capturing realistic behavior of many real-world networks. The research will develop rigorous mathematical methods for analyzing structural and dynamical properties of adaptive networks, and define novel information?theoretic measures for quantifying their complexity.Broader Impact: Our nation?s technological infrastructure of future will depend on our ability to control large?scale, dynamic networks of interconnected heterogeneous entities. This work will help to better understand, characterize, and predict the collective behavior of such networks. The project will train new professionals and scientists in an important interdisciplinary area, and develop a graduate course material on complex adaptive networks.