Extracting the backbone of weighted networks

提取加权网络的主干

• 批准号: 2016320
• 负责人: Zachary Neal
• 金额: $ 14.89万
• 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: 2020
• 资助国家: 美国
• 起止时间: 2020-08-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2016320&HistoricalAwards=false
• 关键词: Extracting; backbone; weighted; networks

In this project, methods to extract the backbone of weighted networks are investigated and computer software to use these methods is developed. Social networks are often complex, with the interactions between actors (e.g., people, organizations, cities) varying in strength, which can be represented by weighted networks. Because weighted networks are challenging to analyze and visualize, it is often useful to focus on their backbones, which contain only the most significant connections. Many approaches to backbone extraction exist, but we know little about whether they work or how to choose one approach over another. By providing guidance on and tools for network backbone extraction, this project will enable researchers to analyze better information-rich network data in a wide range of socially significant contexts, and to communicate their findings more easily to diverse audiences through visualization. The goal of this project is to facilitate researchers’ ability to correctly extract the backbone of weighted networks. Achieving this goal involves six activities. First, bipartite ensemble methods are refined to make them faster and more flexible. Second, the R backbone package is extended to allow the extraction of backbones from all types of weighted networks, to accommodate larger datasets, and to be interoperable with other network analysis packages. Third, backbone methods are compared empirically using benchmark datasets to explore their similarities and differences in practice. Fourth, backbone methods are compared analytically to determine their computational complexities and the functional form of their null edge weight distributions. Fifth, backbone methods are compared numerically using synthetic weighted network data, allowing identification of the conditions under which each method validly reproduces a known ground truth. Finally, training materials are developed to instruct researchers on the selection of backbone methods and on the use of the backbone package for their extraction.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，方法提取加权网络的骨干进行了研究和计算机软件使用这些方法的开发。 社交网络通常是复杂的，参与者之间的交互（例如，人、组织、城市）的强度不同，这可以由加权网络表示。 由于加权网络的分析和可视化具有挑战性，因此专注于其主干通常是有用的，因为主干只包含最重要的连接。 存在许多提取主干的方法，但我们对它们是否有效或如何选择一种方法知之甚少。通过提供网络主干提取的指导和工具，该项目将使研究人员能够在广泛的社会重要背景下更好地分析信息丰富的网络数据，并通过可视化更容易地将他们的发现传达给不同的受众。该项目的目标是促进研究人员正确提取加权网络骨干的能力。实现这一目标需要开展六项活动。首先，改进了二部集成方法，使其更快，更灵活。其次，扩展了R主干包，允许从所有类型的加权网络中提取主干，以适应更大的数据集，并与其他网络分析包互操作。第三，骨干方法进行了实证比较，使用基准数据集，以探讨他们在实践中的异同。第四，骨干方法进行了分析比较，以确定其计算复杂性和功能的形式，他们的零边重量分布。第五，骨干方法进行了数值比较，使用合成加权网络数据，允许识别的条件下，每个方法有效地再现了一个已知的地面真相。最后，开发培训材料来指导研究人员选择主干方法以及使用主干包进行提取。该奖项反映了NSF的法定使命，并且通过使用基金会的知识价值进行评估，被认为值得支持。和更广泛的影响审查标准。