III: Small: Characterizing and exploiting tree-like structure in large social and information networks

III：小型：描述和利用大型社交和信息网络中的树状结构

• 批准号: 1423621
• 负责人: Michael Mahoney
• 金额: $ 50万
• 依托单位: International Computer Science Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-15 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1423621&HistoricalAwards=false
• 关键词: III; Small; Characterizing; exploiting; tree

Recent technological advances have led to an explosive growth of social, biological, and information network data. This has led to a wide range of metrics, e.g., degree distributions, clustering coefficients, homophily measures, and so on, to describe and extract insight from real networks. The hope is that these metrics will help domain scientists to extract information and actionable insight from these networks. This enthusiasm, however, has not yet been fully realized; and an ongoing challenge is to develop finer actionable metrics to understand the properties of real networks. This project will facilitate the development of tools for the extraction of knowledge from large genetic, medical, internet, financial, astronomical, and other scientific network data sets, and it will enhance interdisciplinary education more generally. In more detail, this project will investigate methods to characterize and exploit tree-like structure in real information networks. It will focus on two related but complementary notions of tree-like-ness for graphs; it will use these notions to develop tools to characterize the manner in which real complex networks are tree-like; and it will use this characterization to develop tools for improved analytics on real networks. Particular attention will be paid to how this can shed light on intermediate-scale, i.e., not very small-scale or local and not very large-scale or global, structure in real networks; and the results of this project will provide implementations of algorithms to determine how and where this new understanding can be exploited for domain-specific insight more generally.

最近的技术进步导致了社会，生物学和信息网络数据的爆炸性增长。 这导致了广泛的指标，例如度分布，聚类系数，同质度量等，以描述和提取从真实网络中的见解。 希望这些指标将帮助领域科学家从这些网络中提取信息和可行的见解。 然而，这种热情尚未完全实现。一个持续的挑战是开发更精细的可行指标，以了解真实网络的属性。 该项目将促进开发从大型遗传，医学，互联网，财务，天文和其他科学网络数据集中提取知识的工具，并将更广泛地增强跨学科教育。 更详细地，该项目将调查在真实信息网络中表征和利用类似树状结构的方法。 它将集中在两个相关的但互补的图形的概念上。它将使用这些概念开发工具来表征真正复杂网络类似树的方式；它将使用这种表征来开发用于改进真实网络分析的工具。 将特别注意这一点如何阐明中间规模，即不是很小的规模或本地，而不是非常大的或全球的真实网络中的结构；该项目的结果将提供算法的实现，以确定如何和何处可以为特定于领域的洞察力利用这种新的理解。