AF: Small: Graphs and structures for distance estimation

AF：小：用于距离估计的图形和结构

• 批准号: 1528078
• 负责人: Virginia Williams
• 金额: $ 30万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1528078&HistoricalAwards=false
• 关键词: AF; Small; Graphs; structures; distance

Distance computation and estimation in networks is one of the most basic and fundamental tasks in network analysis. However, storing the distance between every pair of nodes of a graph is infeasible, especially for today?s ?big data.? Small sketches of a graph have been developed so that a good estimate of any pairwise distance can be retrieved from the sketch. This project aims to advance the state-of-the art of such sketches. The main objects of study are spanners, distance oracles and their fault-tolerant variants. A spanner is a sparse subgraph that does not stretch any distance of the original graph by much.  A distance oracle is a data structure that has small space usage and is capable of answering (approximate) distance queries efficiently. Both spanners and distance oracles compress the distance information. The main difference between them is that one can still run graph algorithms on a spanner, but not on a distance oracle, whereas one can obtain any distance from a distance oracle instantly, but in a spanner one would have to actually compute it.In practice, networks are dynamic in nature. To address this, graph sketches would have to tolerate faults, i.e. edge and vertex deletions. There are fault-tolerant versions of spanners and distance oracles-- these structures estimate distances for any given (typically fixed size) subset of failed edges or nodes. This project will provide algorithms for constructing new low-space spanners and oracles with improved guarantees and will strive to develop new techniques for fault-tolerance and distance estimation in general. Spanners and distance oracles have many applications, e.g. in parallel and distributed algorithms for distance computation, network routing, and simulating synchronized protocols in unsynchronized networks. A better understanding of network routing along short paths could guide the design of next-generation Internet protocols. The research is also closely tied to the field of metric embedding, and thus extends beyond the strict boundaries of computer science. Material from this research will be integrated into core undergraduate and graduate courses, and will lead to the development of new courses on the topic. The lecture notes and project materials will be available on the course website for the general public.

网络中的距离计算和估计是网络分析中最基本的任务之一，但是，存储图的每对节点之间的距离是不可行的，尤其是对于当今的“大数据”。已经发展到可以从草图中检索任何成对距离的良好估计。该项目旨在提高此类草图的技术水平。主要研究对象是扳手、距离预言机及其容错能力。变种。扳手是一个稀疏子图，不会拉伸原始图的任何距离。距离预言机是一种空间使用量较小且能够有效回答（近似）距离查询的数据结构。它们之间的主要区别在于，人们仍然可以在 Spanner 上运行图形算法，但不能在距离预言机上运行，​​而人们可以立即从距离预言机获得任何距离，但在 Spanner 中则必须实际计算。实际上，网络本质上是动态的。为了解决这个问题，图草图必须容忍错误，即边缘和顶点删除。扳手和距离预言机有容错版本——这些结构可以估计任何给定的距离。该项目将提供用于构造新的低空间扳手和预言机的算法，并具有改进的保证，并将努力开发用于容错和距离估计的新技术。 Spanner 和距离预言机有许多应用，例如用于距离计算、网络路由和模拟非同步网络中的同步协议的并行和分布式算法，可以指导下一代互联网协议的设计。也与度量嵌入领域密切相关，因此超出了计算机科学的严格界限。这项研究的材料将被整合到核心本科生和研究生课程中，并将导致新课程的开发。讲义和项目材料将在课程网站上向公众提供。