Sampling and Inference in Network Analysis

网络分析中的采样和推理

• 批准号: 1418265
• 负责人: Srinivasan Parthasarathy
• 金额: $ 30万
• 依托单位: Ohio State University
• 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: 2014-08-15 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1418265&HistoricalAwards=false
• 关键词: Sampling; Inference; Network; Analysis

The study of complex networks constitutes an interdisciplinary area of inquiry that transcends traditional knowledge domains by focusing on the fundamental interdependencies of components within various systems-of-interest. Examples abound from social networks to coupled human and natural systems, from financial networks to disease systems, and from telecommunication networks to energy and power systems. It is the interconnection among these components that often sit at the heart of our most vexing global grand challenge problems, including climate change, energy demands, security, health and wellness, and livelihood and poverty. The study of such complex systems and often large scale networks -- understanding their intrinsic properties, changes to their structure over time or due to external factors, multi-scale behavior of individuals to coarser grained modular communities -- can afford important insights to individuals, organizations and society at large when tackling such grand challenge problems. This project seeks to develop robust and scalable sampling methods for the modeling and analysis of large, potentially dynamic, networks. Sampling is often touted as a means to efficiently combat the inherent complexity of estimating the relevant characteristics of a population. Sampling a network is complicated because they are composed of two units (nodes and edges) that are not always nicely nested. A key objective will be to study and provide a sound mathematical basis along with high performance tools for both node-centric and edge-centric sampling methodologies for the analysis and modeling of networks. The objective of realizing high performance tools for real world applications, drawn from social networks and network biology, will be equally significant, and is necessary for sustained innovation of an inter-disciplinary nature. This research will shed light on the theoretical underpinnings of graph sampling and probabilistic inference in both the static and dynamic network contexts. From an educational standpoint, the investigators will train the next generation of graduate students in this interdisciplinary arena and will also actively encourage participation of undergraduates and under-represented minorities.

复杂网络的研究构成了一个跨学科的研究领域，它超越了传统的知识领域，专注于各种感兴趣的系统中组件的基本相互依赖关系。从社交网络到耦合的人类和自然系统，从金融网络到疾病系统，从电信网络到能源和电力系统，这样的例子比比皆是。正是这些组成部分之间的相互联系，往往是我们最棘手的全球重大挑战问题的核心，包括气候变化、能源需求、安全、卫生和健康以及生计和贫困。对这种复杂系统和大规模网络的研究--了解它们的内在属性，它们的结构随时间或外部因素而发生的变化，个体对粗粒度模块化社区的多尺度行为--可以为个人、组织和社会在解决这些重大挑战问题时提供重要的见解。 该项目旨在开发强大的和可扩展的采样方法，用于大型，潜在的动态网络的建模和分析。抽样经常被吹捧为一种有效克服估计人口相关特征的固有复杂性的手段。对网络进行采样是复杂的，因为它们由两个单元（节点和边）组成，这些单元并不总是嵌套得很好。一个关键的目标将是研究和提供一个良好的数学基础沿着与高性能的工具，以节点为中心和边缘为中心的抽样方法的分析和建模的网络。从社交网络和网络生物学中提取高性能工具，用于真实的世界应用，这一目标同样重要，对于跨学科性质的持续创新也是必要的。这项研究将揭示在静态和动态网络环境中的图抽样和概率推理的理论基础。从教育的角度来看，调查人员将在这一跨学科竞技场中培训下一代研究生，并将积极鼓励大学生和代表性不足的少数民族参与。