CAREER: Modeling and Analysis of Data from Massive Graphs

该项目定义了一种处理海量图表的新方法，该方法确定了许多应用程序共同面临的三个基本挑战：规模、动态性和不确定性。该项目提出了通用的、独立于应用程序的图形压缩方案，以总结大规模的海量图形。这些算法应该是高效的，使用少量的空间和时间来产生压缩表示。此外，这些算法应该被证明是正确的。此外，这些工具应适用于动态图形数据。他们应该从历史数据中学习图表的模型。最后，这个项目将设计可以从大量图表的样本中推断图表属性的工具，因为这样的图表不能完全观察到。在可以设计抽样方案的应用中，我们努力做到尽可能有效和高效。brbr我们生活在一个信息时代。在我们许多科技、科学和经济力量的背后，是大量的数据。一种日益重要的数据类型是关系数据或图形数据。这些数据捕获实体如何相互关联、它们如何相互交互，或者对象如何链接在一起。实体之间的所有形式的通信产生图形数据，包括通过互联网中的IP分组对源和目的地IP地址的通信、人们彼此发送电子邮件、相互引用的网页或大型生物系统中彼此相互作用的蛋白质。许多科学、工程和医学应用程序依赖于我们在面对数据变化和不完善信息的情况下快速建模、分析、处理和合成此类数据的能力。事实上，我们的安全和互联网的安全可能取决于我们对实体(无论是人还是IP地址)如何相互作用的理解。我们目前针对关系数据的统计和算法工具不足以处理海量图表。他们没有跟上我们收集海量数据的能力，以及我们准确有效地分析这些数据的需求。我们必须能够对巨大的图形进行建模、压缩并突出显示其重要特性，这些图形随着时间的推移而演变(可能很快)，并且可能捕捉到较大图形的有限视图。该项目旨在开发健壮、高效和可证明正确的方法来管理海量图形。

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.