ITR-(NHS+ASE)-(Sim): Self-Organization of Complex Network Dynamics for Efficiency and Robustness

该奖项是根据信息技术研究招标NSF-04-012提交给材料研究部的提案而颁发的。该奖项涵盖的研究活动属于国家优先领域，“科学与工程的进步”和技术重点领域，“计算建模或模拟研究的创新”。“这个奖项支持网络动力学特性的计算研究。许多自然和人造系统都是复杂网络。 例子包括基础设施网络，如电网、电话线和互联网;运输网络，如高速公路、铁路和航空公司;以及社交网络，如描述非法交易、合作和恐怖分子的网络。 其他复杂网络描述了工程和生物学中的动态自适应控制系统。 所有这些网络都由节点组成，其中连接或交互由链接网格描述。 一般来说，节点的行为将是异质的，并且链路将具有不同的强度或容量。 通常，链路在节点之间传输一些量，包括信息、能量、材料、疾病或影响。 因此，网络通常会发展或构建，以确保传输对于网络本身的变化或其环境或任务的变化是有效和鲁棒的。 在许多自然网络和一些设计的网络中，进化或组织成为高效和健壮的能力是建立在网络本身的动态中的。 理解和控制复杂网络的演化动力学对国家和国土安全以及先进的科学和工程都很重要。 相反，这项研究将探索网络的动态如何自组织，以确保有效和强大的行为。 它还将研究网络的动态如何与其结构的演变有关。 我们的目标是描述和理解如何设计网络动态，以稳健的方式优化和控制其行为。 另一个目标将是相反的，即了解如何禁用网络的行为。 具体来说，这项工作将建立在最近的结果，探索无标度网络中的运输效率，无标度领导结构的出现和集体效率的社交网络，以及渠道化的演变，一种鲁棒性已知是重要的发育生物学，在自适应控制网络。 动态网络的简单模型，旨在捕捉现实世界网络的基本行为将与统计物理的工具和方法，包括大规模的计算机模拟研究。 研究结果将对国家和国土安全具有重要意义，因为它们将帮助我们了解如何设计和建造高效可靠的基础设施和交通网络，以及如何利用网络动态的漏洞来破坏恐怖分子或疾病活动。 研究结果也将通过提高我们对网络自适应控制系统的理解和设计来推进科学和工程。 PI还承诺让代表性不足的群体参与。%

This award was made on a proposal submitted to the Division of Materials Research under the Information Technology Research solicitation NSF-04-012. Research activities covered by this award fall under the National Priority Area, "Advances in Science and Engineering," and the Technical Focus Area, "Innovation in Computational Modeling or Simulation in Research." This award supports computational research in the dynamical properties of networks. Many natural and man-made systems are structured as complex networks. Examples include infrastructure networks, such as the power-grid, phone lines, and the Internet, transportation networks, such as highways, railways, and airways, and social networks, such as those describing acquaintanceships, collaborations, and terrorists. Other complex networks describe dynamic adaptive control systems in both engineering and biology. All of these networks consist of nodes where connections or interactions are described by a mesh of links. Generally, the nodes will behave heterogeneously, and the links will have different strengths or capacities. Typically, the links transport some quantity, including information, power, material, disease, or influence, between the nodes. Therefore, the networks often evolve, or are constructed, to ensure that the transport is efficient and robust to changes in either the network itself or to changes in its environment or task. In many natural networks, and in some designed networks, the capacity to evolve or organize to become efficient and robust is built into the dynamics of the network itself. Understanding and controlling the evolutionary dynamics of complex networks is important to both national and homeland security and to advanced science and engineering.Most existing studies of networks are concerned primarily with their structural properties. This research will, instead, explore how the dynamics of networks can self-organize to ensure efficient and robust behavior. It will also examine how the dynamics of networks is related to the evolution of their structure. The goal will be to characterize and understand how network dynamics can be designed to optimize and control their behavior in a robust way. Another goal will be the opposite, namely to understand how the behavior of a network can be disabled. Specifically, the work will build upon recent results that have explored the efficiency of transport in scale-free networks, the emergence of scale-free leadership structure and of collective efficiency in social networks, and the evolution of canalization, a type of robustness known to be important in developmental biology, in adaptive control networks. Simple models of dynamic networks that aim to capture the essential underlying behavior of real-world networks will be studied with the tools and methods of statistical physics, including large-scale computer simulations. The results will then be compared with real-world networks.The results of the research will be important to national and homeland security because they will help us understand how to design and build efficient and reliable infrastructure and transportation networks, and how terrorist or disease activity may be disrupted by exploiting the vulnerabilities of the network dynamics. The results will also advance science and engineering by improving our understanding and design of adaptive control systems on networks.Graduate students will be involved with this project. The PI also has a commitment to involvement of underrepresented groups.%%%