CAREER: Modeling and Analysis of Data from Massive Graphs

职业：海量图表数据的建模和分析

• 批准号: 0547744
• 负责人: Anna Gilbert
• 金额: $ 40万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-05-15 至 2012-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0547744&HistoricalAwards=false
• 关键词: CAREER; Modeling; Analysis; Data; Massive

This project defines a new approach to massive graphs that identifies three fundamental challenges common to many applications: scale, dynamism, and uncertainty. The project advances graph compression schemes that are universal and independent of the application to summarize massive graphs at large scales. These algorithms should be highly efficient, using a small amount of space and time to produce a compressed representation. Furthermore, these algorithms should be provably correct. In addition, the tools should be adapted to dynamic graph data. They should learn a model of the graph from historical data. Finally, this project will design tools that can infer graph properties from samples of a massive graph, since such a graph cannot be observed in its entirety. In applications where sampling schemes can be devised, we strive to do so as effectively and as efficiently as possible.brbrWe live in an information age. Behind many of our technological, scientific, and economic forces are large volumes of data. An increasingly important type of data is relational data or graph data. These data capture how entities are related to one another, how they interact with one another, or how objects are linked together. All forms of communication amongst entities give rise to graph data, including the communication of source and destination IP addresses via IP packets in the Internet, people sending email to one another, web pages referring to one another, or proteins interacting with one another in large biological systems. Many scientific, engineering, and medical applications depend on our abilities to model, to analyze, to process, and to synthesize this type of data quickly, in the face of changes to the data, and under imperfect information. Indeed, our security and the security of the Internet may hinge upon our understanding of how entities (be they people or IP addresses) interact with one another. Our current statistical and algorithmic tools for relational data are not adequate for massive graphs. They have not kept pace with our ability to collect enormous amounts of data and our need to accurately and efficiently analyze that data. We must be able to model, to compress, and to highlight the important features of graphs that are gigantic, that evolve over time (perhaps quickly), and that may capture a limited view of a larger graph. This project aims to develop robust, highly efficient, and provably correct methods for managing massive graphs.

该项目定义了一种新的方法来确定许多应用程序常见的三个基本挑战：规模，动态和不确定性。 该项目推进图形压缩方案，这些计划是通用的，并且与应用程序无关，以总结大规模的大量图。 这些算法应使用少量的空间和时间来产生压缩表示形式。 此外，这些算法应证明是正确的。 另外，该工具应适用于动态图数据。 他们应该从历史数据中学习图形的模型。 最后，该项目将设计可以从大量图的样品中推断出图形属性的工具，因为无法完整观察到这样的图形。 在可以设计采样方案的应用中，我们努力尽可能有效地做到这一点。Brbrbrwe生活在信息时代。 我们许多技术，科学和经济力量的背后是大量数据。 越来越重要的数据类型是关系数据或图形数据。这些数据捕获了实体如何相互关联，它们如何相互作用或对象如何链接在一起。 实体之间的所有形式的通信都会引起图形数据，包括通过互联网中的IP数据包进行源和目标IP地址的通信，人们彼此发送电子邮件，彼此引用的网页，或在大型生物系统中相互交互的蛋白质。 许多科学，工程和医学应用都取决于我们建模，分析，处理和综合此类数据的能力，面对数据的变化以及不完美的信息。 确实，我们的安全性和互联网的安全性可能取决于我们对实体（他们或IP地址）如何相互作用的理解。 我们目前用于关系数据的统计和算法工具不足以适合大量图形。 他们没有跟上我们收集大量数据的能力，以及我们需要准确有效地分析该数据的能力。 我们必须能够建模，压缩并突出显示巨大图形的重要特征，这些图形随着时间的流逝而发展（也许很快），并且可能会捕获较大图形的有限视图。 该项目旨在开发可靠，高效且可证明的正确方法的方法。