The Analysis and Modeling of Large Linked Networks

大型链接网络的分析与建模

• 批准号: 0514429
• 负责人: John Hopcroft
• 金额: $ 47.5万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-04-01 至 2010-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0514429&HistoricalAwards=false
• 关键词: Analysis; Modeling; Large; Linked; Networks

The goal of this research project is to develop methods for uncovering information hidden in large linked real-world networks. The approach is based on spectral analysis techniques that were previously developed for sparse graphs, and are extended to work on complex networks in order to defuse the "majority rule" where the stronger areas of the network structure dominate the overall structure. The outcome is that the network analysis will not be dominated by the stronger nodes obscuring weaker nodes that are structurally significant in the network. The project also develops methods for tracking changes in such graphs in order to discover new clusters, e.g., novel research communities. Clusters that occur in most of these networks are the natural communities, and provide a basic unit of analysis for tracking over time. Finally, the network analysis and modeling methods are extended to heterogeneous graphs that include multiple link types. The work combines the development of formal models with the analysis of empirical data. The research project provides an excellent educational platform, introducing undergraduate and graduate students to statistical techniques and algorithmic methods. The theoretical results will find applications in a broad range of areas, including Web searching, citation analysis, data mining and information discovery, social networks analysis, or power grid management. The project's Web site (http://www.cs.cornell.edu/jeh/linked.htm) will provide access to the project's results and resulting software.

该研究项目的目标是开发方法来揭示隐藏在大型链接现实世界网络中的信息。该方法基于先前为稀疏图开发的谱分析技术，并扩展到复杂网络上的工作，以化解“多数规则”，即网络结构的较强区域主导整体结构。结果是网络分析不会被较强的节点所主导，从而掩盖网络中结构上重要的较弱的节点。该项目还开发了跟踪此类图表变化的方法，以发现新的集群，例如新的研究社区。大多数这些网络中出现的集群是自然群落，并为随时间的推移提供跟踪的基本分析单位。最后，将网络分析和建模方法扩展到包括多种链路类型的异构图。这项工作将形式模型的开发与经验数据的分析结合起来。该研究项目提供了一个优秀的教育平台，向本科生和研究生介绍统计技术和算法方法。理论结果将在广泛的领域得到应用，包括网络搜索、引文分析、数据挖掘和信息发现、社交网络分析或电网管理。该项目的网站 (http://www.cs.cornell.edu/jeh/linked.htm) 将提供对该项目结果和最终软件的访问。