NetSE: Medium: Modeling and Analysis of Network Dynamics

NetSE：媒介：网络动态建模与分析

• 批准号: 1065133
• 负责人: Donald Towsley
• 金额: $ 78.02万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-06-01 至 2015-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1065133&HistoricalAwards=false
• 关键词: NetSE; Medium; Modeling; Analysis; Network

There has been a rapid increase in the number and types of digital networks over the last 2 decades beginning, of course with the Internet, its constituent networks, and the World Wide Web. We now have a wide range of on-line social networks (OSNs) such as Facebook, Twitter, mobile ad hoc networks (MANETs) and delay tolerant networks (DTNs). These networks pervade all aspects of our lives and provide a growing range of services in commerce, business, communications, and connectivity. Many of these networks are continually in a dynamic state of flux and/or extremely large. For example, the topology of a MANET or a DTN is constantly changing, and that of an OSN such as Facebook is rapidly growing and evolving. The modeling, analysis, and measurement of such networks are challenging, due to their dynamism and sizes. As a consequence, traditional mathematical techniques are not suitable and fundamentally new techniques are needed.This research will focus on three interrelated problems. The first is to develop useful and accurate models that capture the dynamics of today?s social and technological networks. The second is to develop and study a class of techniques for characterizing and searching such networks. These techniques are based on a very simple mechanism, namely to let an agent explore the network by randomly choosing where to go. This is known as a random walk in the mathematics literature and has been shown to exhibit desirable search and characterization properties in static networks that shows promise in dynamic networks. Last, many static networks exhibit what is known as a power law, namely that the distribution for the number of neighbors of a node roughly decays as k−a where k is the number of neighbors and a is a positive constant greater than one. The third problem is to understand what constitutes a power law in a dynamic network, how this power law comes to be, and what implications there might be regarding the health of the network.Broader Impact. The work will positively impact society by providing a deeper understanding of and a set of tools for managing and monitoring digital networks such as MANETs and OSNs. The project includes a comprehensive dissemination plan including public release of tools for network characterization. The education plan includes cross-specialty seminars, undergraduate involvement in research through a REU site, and international outreach to South America.

过去 20 年来，数字网络的数量和类型迅速增加，当然是从互联网及其组成网络和万维网开始的。我们现在拥有广泛的在线社交网络 (OSN)，例如 Facebook、Twitter、移动自组织网络 (MANET) 和延迟容忍网络 (DTN)。这些网络渗透到我们生活的各个方面，并在商业、商业、通信和连接方面提供越来越多的服务。许多这样的网络持续处于动态变化和/或非常大的状态。例如，MANET 或 DTN 的拓扑不断变化，而 Facebook 等 OSN 的拓扑正在快速增长和发展。由于此类网络的动态性和规模，其建模、分析和测量具有挑战性。因此，传统的数学技术不再适用，需要全新的技术。本研究将集中于三个相互关联的问题。首先是开发有用且准确的模型来捕捉当今社会和技术网络的动态。第二个是开发和研究一类用于表征和搜索此类网络的技术。这些技术基于一个非常简单的机制，即让代理通过随机选择去哪里来探索网络。这在数学文献中被称为随机游走，并且已被证明在静态网络中表现出理想的搜索和表征特性，这在动态网络中表现出了希望。最后，许多静态网络表现出所谓的幂律，即节点邻居数量的分布大致衰减为 k−a，其中 k 是邻居数量，a 是大于 1 的正常数。第三个问题是了解动态网络中幂律的构成、幂律是如何形成的以及对网络健康可能产生的影响。更广泛的影响。这项工作将通过提供对 MANET 和 OSN 等数字网络的更深入理解和一套管理和监控数字网络的工具，对社会产生积极影响。该项目包括一个全面的传播计划，其中包括公开发布网络表征工具。该教育计划包括跨专业研讨会、本科生通过 REU 网站参与研究以及向南美洲进行国际推广。