# ReeFRAME: Reeb Graph based Trajectory Analysis Framework to Capture Top-Down and Bottom-Up Patterns of Life

arXiv: https://arxiv.org/abs/2410.14913 · HTML: https://mlsanity.com/paper/2410.14913/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 19 Oct 2024.

## Builds on (most cited first, 4 of 4)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [A Survey on Deep Learning for Human Mobility](https://mlsanity.com/paper/2012.02825/) (2020)
- [Trajectory Grouping Structures](https://mlsanity.com/paper/1303.6127/) (2013)
- [A computational geometry approach for modeling neuronal fiber pathways](https://mlsanity.com/paper/2108.01175/) (2021)

## Built on by (most cited first, 2 of 2)

- [GPS-MTM: Capturing Pattern of Normalcy in GPS-Trajectories with self-supervised learning](https://mlsanity.com/paper/2509.24031/) (2025)
- [Markovian Reeb Graphs for Simulating Spatiotemporal Patterns of Life](https://mlsanity.com/paper/2510.03152/) (2025)

## 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.