The Analysis and Modeling of Large Linked Networks

该研究项目的目标是开发揭示隐藏在大型链接现实世界网络中的信息的方法。该方法基于先前为稀疏图开发的频谱分析技术，并扩展到复杂网络上，以消除“多数规则”，即网络结构的较强区域主导整体结构。其结果是，网络分析不会被较强的节点所主导，从而掩盖了网络中在结构上重要的较弱节点。该项目还开发了跟踪这些图变化的方法，以便发现新的集群，例如，新的研究社区。在这些网络中出现的集群是自然群落，并为跟踪提供了一个基本的分析单位。最后，将网络分析和建模方法扩展到包含多种链路类型的异构图。这项工作将正式模型的发展与经验数据的分析结合起来。该研究项目为本科生和研究生介绍统计技术和算法方法提供了一个良好的教育平台。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、社会网络分析或电网管理。项目的网站（http://www.cs.cornell.edu/jeh/linked.htm）将提供对项目结果和结果软件的访问。

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.