ReeFRAME: Reeb Graph based Trajectory Analysis Framework to Capture Top-Down and Bottom-Up Patterns of Life
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Abstract (arXiv)
In this paper, we present ReeFRAME, a scalable Reeb graph-based framework designed to analyze vast volumes of GPS-enabled human trajectory data generated at 1Hz frequency. ReeFRAME models Patterns-of-life (PoL) at both the population and individual levels, utilizing Multi-Agent Reeb Graphs (MARGs) for population-level patterns and Temporal Reeb Graphs (TERGs) for individual trajectories. The framework's linear algorithmic complexity relative to the number of time points ensures scalability for anomaly detection. We validate ReeFRAME on six large-scale anomaly detection datasets, simulating real-time patterns with up to 500,000 agents over two months.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 4 of its 18 references that are in the map. First posted on arXiv 19 Oct 2024.
Builds on
4 of 18 references.
References · 4
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2020A Survey on Deep Learning for Human Mobilitycited by 87
- 2013Trajectory Grouping Structurescited by 2
- 2021A computational geometry approach for modeling neuronal fiber pathwayscited by 1
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Citing papers per year: 2025: 2.
2 papers.