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

NetSE：小型：复杂自适应网络：生成模型和统计分析

• 批准号: 0916534
• 负责人: Herbert Schorr
• 金额: $ 44.51万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0916534&HistoricalAwards=false
• 关键词: NetSE; Small; Complex; Adaptive; Networks

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.

我们的社会正变得越来越依赖于各种技术网络，如交通系统，电力？随着这些网络的发展和复杂性的增长，它们的动态行为变得难以理解和预测。这个项目将开发一个通用的框架来模拟这种复杂的自适应网络的增长和进化，基于相互作用的随机过程的概念。这种方法背后的直觉是，形成网络的相互作用是由随机过程的集体状态所告知的，而这些过程本身则受到形成网络结构的影响。这种反馈机制的存在对于捕捉许多现实世界网络的真实行为至关重要。该研究将开发严格的数学方法来分析自适应网络的结构和动态特性，并定义新的信息？量化其复杂性的理论措施。更广泛的影响：我们的国家？未来的技术基础设施将取决于我们控制大？规模，相互连接的异构实体的动态网络。 这项工作将有助于更好地理解，描述和预测这些网络的集体行为。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，并编写关于复杂适应网络的研究生课程材料。