Scalable Statistical Inference in Small-World Networks

小世界网络中的可扩展统计推断

基本信息

批准号：
1916378
负责人：
Sebastien Roch
金额：
$ 30万
依托单位：
University of Wisconsin-Madison
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-15 至 2023-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1916378&HistoricalAwards=false
关键词：
Scalable Statistical Inference Small World

项目摘要

Social networks often exhibit "small-world features". For instance, friends typically share many common friends, but most individuals have a limited number of close acquaintances irrespective of the size of the network. Another well-documented feature of such networks is the "six degrees of separation property", whereby most people are a small number of social connections away from one another. Despite their ubiquity, common statistical models of complex networks do not typically generate graphs with these properties. Therefore, the main goal of this project is to address the general lack of plausible and tractable statistical models of small-world networks. Specifically, the PIs will develop a novel framework for the inference of these networks, including statistical models, fast and scalable algorithms, as well as supporting theory. These models and methods will be empirically validated through the development and deployment of techniques that sample large graphs in ways that helps assess them. This new understanding will contribute to ongoing interdisciplinary collaborations in journalism, health care, and law. Existing probabilistic constructions of small-world networks, i.e., random graphs exhibiting low diameter, sparsity and transitivity, tend to be ad-hoc and, hence, often not suitable for statistical inference. In this project, the PIs will formulate and analyze interpretable statistical models of small-world networks; and develop scalable statistical inference for such models based on both spectral techniques and local sampling. For this purpose, the PIs will develop and explore a family of network models with high-dimensional latent features. The PIs will analyze how traditional algorithms perform in this regime, and will develop and analyze local sampling algorithms, such as respondent-driven sampling. The information-theoretic limit of community detection will also be studied. This grant will support the development of two courses aimed at intermediate undergraduates in UW- Madison's new undergraduate data science degree. These courses will aim to broaden engagement in both data science and social network analysis. This grant will also support the training of PhD students in both Statistics and Mathematics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

社交网络经常表现出“小世界特征”。例如，朋友通常有许多共同的朋友，但无论网络有多大，大多数人的熟人数量都是有限的。这种网络的另一个有据可查的特征是“六度分离属性”，即大多数人彼此之间存在少数社会联系。尽管它们无处不在，但复杂网络的常见统计模型通常不会生成具有这些属性的图。因此，该项目的主要目标是解决小世界网络普遍缺乏合理且易于处理的统计模型的问题。具体来说，PI 将为这些网络的推理开发一个新颖的框架，包括统计模型、快速且可扩展的算法以及支持理论。这些模型和方法将通过开发和部署以有助于评估大图的方式对大图进行采样的技术来进行实证验证。这种新的理解将有助于新闻、医疗保健和法律领域持续的跨学科合作。小世界网络的现有概率构造，即表现出低直径、稀疏性和传递性的随机图，往往是临时的，因此通常不适合统计推断。在这个项目中，PI将制定和分析小世界网络的可解释统计模型；并基于谱技术和局部采样为此类模型开发可扩展的统计推断。为此，PI 将开发和探索一系列具有高维潜在特征的网络模型。 PI 将分析传统算法在这种情况下的表现，并将开发和分析本地抽样算法，例如受访者驱动的抽样。社区检测的信息论极限也将被研究。这笔赠款将支持开发两门针对威斯康星大学麦迪逊分校新本科数据科学学位中级本科生的课程。这些课程旨在扩大对数据科学和社交网络分析的参与。这笔赠款还将支持统计学和数学方面的博士生培训。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Sufficient condition for root reconstruction by parsimony on binary trees with general weights

一般权二叉树简约重建根的充分条件

DOI：
10.1214/21-ecp423
发表时间：
2021-01
期刊：
Electronic Communications in Probability
影响因子：
0.5
作者：
Roch, Sebastien;Wang, Kun
通讯作者：
Wang, Kun

Targeted sampling from massive block model graphs with personalized PageRank

通过个性化 PageRank 从海量块模型图中进行有针对性的采样

DOI：
10.1111/rssb.12349
发表时间：
2019-10-04
期刊：
Journal of the Royal Statistical Society: Series B (Statistical Methodology)
影响因子：
0
作者：
Fan Chen;Yini Zhang;Karl Rohe
通讯作者：
Karl Rohe

Impossibility of Consistent Distance Estimation from Sequence Lengths Under the TKF91 Model

TKF91模型下不可能根据序列长度进行一致的距离估计