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CAREER: Scalable Algorithms for Spectral Analysis of Massive Networked Systems

职业：大规模网络系统频谱分析的可扩展算法

基本信息

批准号：
1651433
负责人：
VICTOR PRECIADO
金额：
$ 50万
依托单位：
University of Pennsylvania
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-03-01 至 2022-02-28
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1651433&HistoricalAwards=false
关键词：
CAREER Scalable Algorithms Spectral Analysis

项目摘要

The Internet, social networks, and genetic networks are examples of large-scale systems composed by a large number of units coupled through a complex network of interactions. This proposal aims to improve our understanding of the relationship between the structure of a network and the performance of networked dynamical processes, such as the spread of diseases in human contact networks, the propagation of information in online social networks, and coordination protocols in robotic networks. From an engineering perspective, a central focus of this work will be the development of efficient strategies to design secure and efficient critical networked infrastructures. In particular, those factors that make a network efficient and/or resilient with respect to a particular dynamical process will be explored. For example, the following questions will be studied: What structural factors make a communication network efficient in the coordination of a group of robotic agents? What network properties are useful while containing the spread of a disease in a human contact network? Since networks are ubiquitous across science and engineering, developing efficient tools for network analysis and design is of great relevance to many scientific disciplines. The proposed research program will be complemented with a comprehensive educational agenda spanning K-12, undergraduate, and graduate level education at the University of Pennsylvania. At the level of K-12 education, the PI will teach a three-week intense course about Engineering Complex Networks to encourage high-school students to further pursue an education in STEM-related fields. This project would also support and train doctorate students in the field of complex networks; as well as undergraduate students working on short-term projects, with an emphasis on increasing retention rates of under-represented minorities in engineering.During the last decade, Network Science has matured into an established research field, providing a plethora of tools for modeling and analyzing complex systems. In particular, the field of spectral graph theory has been instrumental in the development of a wide array of powerful network analysis techniques, such as spectral graph partitioning, community detection, and ranking techniques (including Google PageRank). Furthermore, spectral-graph properties are directly related to the dynamical behavior of many networked processes, such as synchronization of oscillators, multi-agent coordination, and viral spreading processes. The goal of this project is to develop a novel computational framework based on recent results from algebraic graph theory and real algebraic geometry to infer global spectral properties of dynamical relevance from local structural information. Theoretical advancements in this proposal will go hand-in-hand with the development of scalable algorithms for spectral analysis of massive networked systems. Furthermore, an important part of this proposal will be focused on developing efficient strategies to design secure and efficient networked dynamical systems. Examples of particular design problems that will be considered are the design of efficient topologies to facilitate in-network coordination in multi-agent robotic systems, as well as the design of network interventions to contain viral processes in human contact networks. During the course of this proposal, the PI will address a number of fundamental open theoretical problems, as well as explore the application of newly developed techniques to a diverse array of network control problems in collaborations with domain-specific experts.

互联网、社会网络和遗传网络都是由大量单位通过复杂的相互作用网络耦合而组成的大型系统的例子。该建议旨在提高我们对网络结构与网络动态过程性能之间关系的理解，例如人类接触网络中的疾病传播，在线社交网络中的信息传播以及机器人网络中的协调协议。从工程的角度来看，这项工作的中心焦点将是开发有效的策略来设计安全和高效的关键网络基础设施。特别是，那些使网络高效和/或弹性相对于一个特定的动态过程的因素将被探索。例如，将研究以下问题：在一组机器人代理的协调中，哪些结构因素使通信网络有效？在人类接触网络中控制疾病传播时，哪些网络特性是有用的？由于网络在科学和工程中无处不在，因此开发用于网络分析和设计的有效工具与许多科学学科具有很大的相关性。拟议的研究计划将与宾夕法尼亚大学涵盖K-12，本科和研究生水平教育的综合教育议程相辅相成。在K-12教育层面，PI将教授为期三周的工程复杂网络强化课程，以鼓励高中生进一步接受stem相关领域的教育。该项目还将支持和培养复杂网络领域的博士生；以及从事短期项目的本科生，重点是提高工程领域代表性不足的少数族裔的留校率。在过去的十年中，网络科学已经发展成为一个成熟的研究领域，为复杂系统的建模和分析提供了大量的工具。特别是，谱图理论领域在开发一系列强大的网络分析技术方面发挥了重要作用，例如谱图划分、社区检测和排名技术（包括b谷歌PageRank）。此外，谱图特性与许多网络过程的动态行为直接相关，如振荡器的同步、多智能体协调和病毒传播过程。该项目的目标是基于代数图论和真实代数几何的最新结果开发一种新的计算框架，以从局部结构信息推断动态相关的全局谱特性。本提案的理论进步将与大规模网络系统频谱分析的可扩展算法的发展携手并进。此外，本提案的一个重要部分将集中于开发有效的策略来设计安全高效的网络动力系统。将考虑的特定设计问题的例子是高效拓扑的设计，以促进多智能体机器人系统的网络内协调，以及网络干预的设计，以遏制人类接触网络中的病毒过程。在该提案的过程中，PI将解决一些基本的开放理论问题，并与特定领域的专家合作，探索新开发的技术在各种网络控制问题中的应用。