权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

HNDS-R: Extracting the Backbone of Unweighted Networks

HNDS-R：提取未加权网络的主干

基本信息

批准号：
2211744
负责人：
Zachary Neal
金额：
$ 10.9万
依托单位：
Michigan State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-15 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2211744&HistoricalAwards=false
关键词：
HNDS Extracting Backbone Unweighted Networks

项目摘要

In this project, methods are developed for extracting the backbone from dense, unweighted networks to facilitate their analysis. Networks influence all domains of society, including the diffusion of information and misinformation, the contagion of illness, the passage of legislation, the emergence and maintenance of norms, and the formation of close relationships. In many cases, these networks can be difficult to analyze because they contain so many relationships, and because the strength of these relationships is unknown. Backbone extraction involves identifying and retaining only the most important relationships, which yields a simpler network that can be more readily analyzed and visualized. In this project, in addition, the widely employed backbone software is extended to include these newly developed methods so that researchers can use them easily. This work facilitates the analysis of networks that arise in many different contexts and are studied in many different fields. It also involves the development of training materials to guide researchers in the selection of backbone methods.Development of methods for extracting the backbone from unweighted networks proceeds in three stages. First, the common steps involved in existing backbone extraction methods are identified. Next, each of these steps is implemented into a new function in the backbone package for R. This function allows for the application of both existing backbone models and new backbone models described by novel recombinations of common steps. Finally, the performance of each existing and each new backbone model is evaluated by extracting the backbone from simulated dense unweighted networks that have been embedded with hidden community or hub structures. Once the most promising backbone models are identified, their software implementations are refined for scalability, allowing them to be applied to large networks. Additionally, software documentation and training materials are prepared that provide guidance to researchers using backbone models and the associated backbone package.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.

在这个项目中，开发了从密集的未加权网络中提取主干的方法，以便于分析。网络影响社会的所有领域，包括信息和错误信息的传播、疾病的传染、立法的通过、规范的产生和维持以及密切关系的形成。在许多情况下，这些网络可能很难分析，因为它们包含如此多的关系，而且这些关系的强度是未知的。主干提取只涉及识别和保留最重要的关系，这会产生一个更简单的网络，可以更容易地分析和可视化。此外，在本计画中，广泛使用的主干软体也加以扩充，以包含这些新开发的方法，使研究人员能容易地使用它们。这项工作有助于分析在许多不同背景下出现的网络，并在许多不同的领域进行研究。它还涉及开发培训材料，以指导研究人员选择主干方法。开发从未加权网络中提取主干的方法分三个阶段进行。首先，确定了现有主干提取方法中涉及的常见步骤。接下来，这些步骤中的每一个都被实现到R的主干包中的新函数中。此功能允许应用现有的骨干模型和新的骨干模型描述的新的重组共同的步骤。最后，每个现有的和每个新的骨干模型的性能进行评估，通过提取的骨干，从模拟密集的未加权网络，已嵌入隐藏的社区或枢纽结构。一旦确定了最有前途的骨干模型，它们的软件实现将被改进以实现可扩展性，从而使它们能够应用于大型网络。此外，还准备了软件文档和培训材料，为使用主干模型和相关主干包的研究人员提供指导。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。