# Identifying and Characterising Higher Order Interactions in Mobility Networks Using Hypergraphs

arXiv: https://arxiv.org/abs/2503.18572 · HTML: https://mlsanity.com/paper/2503.18572/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 24 Mar 2025.

## Builds on (most cited first, 5 of 5)

- [Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information](https://mlsanity.com/paper/2012.00489/) (2020)
- [Epidemics on Hypergraphs: Spectral Thresholds for Extinction](https://mlsanity.com/paper/2103.07319/) (2021)
- [Spatial-Temporal Interplay in Human Mobility: A Hierarchical Reinforcement Learning Approach with Hypergraph Representation](https://mlsanity.com/paper/2312.15717/) (2023)
- [Urban Sensing based on Mobile Phone Data: Approaches, Applications and Challenges](https://mlsanity.com/paper/2008.12992/) (2020)
- [Learning Mobility Flows from Urban Features with Spatial Interaction Models and Neural Networks](https://mlsanity.com/paper/2004.11924/) (2020)

## Abstract (arXiv)

Understanding human mobility is essential for applications ranging from urban planning to public health. Traditional mobility models such as flow networks and colocation matrices capture only pairwise interactions between discrete locations, overlooking higher-order relationships among locations (i.e., mobility flow among two or more locations). To address this, we propose co-visitation hypergraphs, a model that leverages temporal observation windows to extract group interactions between locations from individual mobility trajectory data. Using frequent pattern mining, our approach constructs hypergraphs that capture dynamic mobility behaviors across different spatial and temporal scales. We validate our method on a publicly available mobility dataset and demonstrate its effectiveness in analyzing city-scale mobility patterns, detecting shifts during external disruptions such as extreme weather events, and examining how a location's connectivity (degree) relates to the number of points of interest (POIs) within it. Our results demonstrate that our hypergraph-based mobility analysis framework is a valuable tool with potential applications in diverse fields such as public health, disaster resilience, and urban planning.