Synthetic Geosocial Network Generation

In the past the blog has explored the creation of social networks for models. Keeping with this vain of research, I was fortunate to work with Ketevan Gallagher, Taylor Anderson and Andreas Züfle to consider the role of location of individuals when generating social networks. This work has resulted in a new paper entitled "Synthetic Geosocial Network Data Generation"  which was presented at the 7th ACM SIGSPATIAL Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising (LocalRec 2023). If this sounds of interest, below you can read the abstract to the paper, see some the generated geosoical networks and find the full reference and link to the paper. In addition to this, the Python code and data used to generate the networks is available at https://github.com/KetevanGallagher/Synthetic-Geosocial-Networks.

Abstract: Generating synthetic social networks is an important task for many problems that study humans, their behavior, and their interactions. Geosocial networks enrich social networks with location information. Commonly used models to generate synthetic social networks include the classical Erdos-Renyi, Barabasi-Albert, and Watts-Strogatz models. However, these classic social network models do not consider the location of individuals. Real-world geosocial networks do exhibit a strong spatial autocorrelation, thus having a higher likelihood of a social connection between agents that are spatially close. As such, recent variants of the three classical models have been proposed to consider location information. Yet, these existing solutions assume that individuals are located on a uniform lattice and exhibit certain limitations when applied to real-world data that exhibits clusters. In this work, we discuss these limitations and propose new approaches to extend the three classic social network generation models to geosocial networks. Our experiments show that our generated synthetic geosocial networks address the shortcomings of the state-of-the-art models and generate realistic geosocial networks that exhibit high similarity to real-world geosocial networks. 

Keywords: Geosocial Networks, Network Generation, Synthetic Social Networks, Erdos-Renyi, Watts-Strogatz, Barabasi-Albert.

Real- World Geosocial Network using Facebook Social Connectedness Data between Zone Improvement Plan (ZIP) Region Centroids for the State of Virginia, USA.

Geosocial graphs using Virginia ZIP code data.

Graphs using Fairfax Census Tract data.

Full Referece:

Gallagher, K., Anderson, T., Crooks, A.T. and Züfle, A. (2023), Synthetic Geosocial Network Data Generation, Proceedings of the 7th ACM SIGSPATIAL Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising (LocalRec 2023), Hamburg, Germany. (pdf) (presentation)