# High-Dimensional Change Point Detection via Graph Spanning Ratio

arXiv: https://arxiv.org/abs/2512.07541 · HTML: https://mlsanity.com/paper/2512.07541/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 30 references that are in the map.
- First posted on arXiv 8 Dec 2025.

## Builds on (most cited first, 3 of 3)

- [High-dimensional changepoint estimation via sparse projection](https://mlsanity.com/paper/1606.06246/) (2016)
- [Minimax rates in sparse, high-dimensional changepoint detection](https://mlsanity.com/paper/1907.10012/) (2019)
- [High-Dimensional Changepoint Detection via a Geometrically Inspired Mapping](https://mlsanity.com/paper/2001.05241/) (2020)

## Abstract (arXiv)

Inspired by graph-based methodologies, we introduce a novel graph-spanning algorithm designed to identify changes in both offline and online data across low to high dimensions. This versatile approach is applicable to Euclidean and graph-structured data with unknown distributions, while maintaining control over error probabilities. Theoretically, we demonstrate that the algorithm achieves high detection power when the magnitude of the change surpasses the lower bound of the minimax separation rate, which scales on the order of √nd. Our method outperforms other techniques in terms of accuracy for both Gaussian and non-Gaussian data. Notably, it maintains strong detection power even with small observation windows, making it particularly effective for online environments where timely and precise change detection is critical.