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

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.