HNDS-R: Extracting the Backbone of Unweighted Networks

在这个项目中，开发了从密集的、未加权的网络中提取主干的方法，以便于分析。网络影响着社会的各个领域，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。主干提取涉及仅识别和保留最重要的关系，从而产生更简单的网络，更易于分析和可视化。在这个项目中，此外，广泛使用的骨干软件被扩展到包括这些新开发的方法，以便研究人员可以很容易地使用它们。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。它还包括开发培训材料以指导研究人员选择主干方法。从未加权网络中提取主干的方法的开发分三个阶段进行。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被实现为R的主干包中的新功能。该功能允许应用现有主干模型和由公共步骤的新颖重新组合所描述的新主干模型。最后，通过从嵌入了隐藏社区或中心结构的模拟密集未加权网络中提取骨干来评估每个现有和每个新骨干模型的性能。一旦确定了最有希望的主干模型，它们的软件实施就会针对可扩展性进行改进，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，为使用骨干模型和相关骨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智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.