Identifying and Characterising Higher Order Interactions in Mobility Networks Using Hypergraphs

2025-03-24

AI Summary

Traditional mobility models—such as flow networks and co-location matrices—capture only pairwise location interactions, failing to represent higher-order mobility patterns involving coordinated visits across three or more points of interest (POIs). To address this limitation, we propose a co-visit hypergraph model that explicitly encodes individual trajectories as dynamic hypergraphs. Our method segments trajectories via sliding time windows, mines frequent co-visit patterns, and constructs hypergraphs where hyperedges represent simultaneous visits to ≥3 POIs. This approach transcends conventional spatiotemporal granularity constraints and uncovers a statistically significant positive correlation between POI density and hypergraph node connectivity (p < 0.01). Furthermore, on public mobility datasets, our model successfully detects abrupt urban mobility anomalies triggered by extreme weather events. These results demonstrate the critical value of higher-order structural representations for modeling and interpreting complex human mobility behavior.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

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.

Problem

• Captures higher-order mobility interactions beyond pairwise models
• Analyzes dynamic mobility patterns across spatial-temporal scales
• Links location connectivity to points of interest distribution

Innovation

• Co-visitation hypergraphs model group interactions
• Frequent pattern mining constructs dynamic hypergraphs
• Validated on city-scale mobility dataset analysis