HNDS-I: Using Hypergraphs to Study Spreading Processes in Complex Social Networks

诸如在社交网络上传播错误信息之类的过程往往依赖于不止一个人之间的互动。例如，一对一的讨论将影响一个人的意见不同于群体同伴压力。许多人与许多其他人同时交互的连接网络可以用超图表示。这个项目将开发一个开放的软件库，使研究者能够研究社会超图的结构如何影响诸如疾病、信息、激进化和观点的传播等过程。虽然有许多公开可用的软件工具用于分析网络，但用于研究超图的资源非常少且分散。这个项目开发的库将使社会科学和其他领域的研究人员更容易进行社会超图计算模型的研究，而不需要从头开发自己的软件。该项目将开发一个开源python库，用于分析和可视化社交超图上的传播过程。该库将允许研究人员导入现有的数据集，并使用各种模型结构创建合成超图。该库还将允许他们在这些网络上模拟各种可定制的传播过程，并将这些模拟的结果可视化。其他功能将包括创建具有社区结构和连接的超图的可能性，这些超图取决于个人的特征，如年龄、性别和社会经济地位。该项目的成果将与太平洋西北国家实验室开发的HyperNetX库进行对接。该奖项反映了美国国家科学基金会的法定使命，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。

Processes such as the spread of misinformation on social networks often depend on interactions between more than one person. For example, a one-on-one discussion will affect a person’s opinions differently than group peer pressure. Networks of connections between many people that interact simultaneously with many others can be represented using hypergraphs. This project will develop an open software library to allow investigators to study how the structure of social hypergraphs affects processes such as the spread of disease, information, radicalization, and opinions. While there are many publicly available software tools for the analysis of networks, there are very few and scattered resources to study hypergraphs. The library developed in this project will make it easier for investigators in the social sciences and other areas to do research on computational models of social hypergraphs without the need to develop their own software from scratch.This project will develop an open-source python library for the analysis and visualization of spreading processes on social hypergraphs. The library will allow researchers to import existing data sets and to create synthetic hypergraphs using various model structures. The library will also allow them to simulate various customizable spreading processes on these networks, and to visualize the outcomes of these simulations. Additional features will include the possibility of creating hypergraphs with community structure and connections that depend on an individual’s features such as age, gender, and socioeconomic status. The results of this project will be interfaced with the HyperNetX library developed by Pacific Northwest National Laboratory.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.