# Online non-parametric change-point detection for heterogeneous data streams observed over graph nodes

arXiv: https://arxiv.org/abs/2110.10518 · HTML: https://mlsanity.com/paper/2110.10518/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 20 Oct 2021.

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

- [Graph Signal Processing: Overview, Challenges and Applications](https://mlsanity.com/paper/1712.00468/) (2017)
- [Stationary signal processing on graphs](https://mlsanity.com/paper/1601.02522/) (2016)
- [Block stochastic gradient iteration for convex and nonconvex optimization](https://mlsanity.com/paper/1408.2597/) (2014)
- [High-dimensional, multiscale online changepoint detection](https://mlsanity.com/paper/2003.03668/) (2020)

## Abstract (arXiv)

Consider a heterogeneous data stream being generated by the nodes of a graph. The data stream is in essence composed by multiple streams, possibly of different nature that depends on each node. At a given moment τ, a change-point occurs for a subset of nodes C, signifying the change in the probability distribution of their associated streams. In this paper we propose an online non-parametric method to infer τ based on the direct estimation of the likelihood-ratio between the post-change and the pre-change distribution associated with the data stream of each node. We propose a kernel-based method, under the hypothesis that connected nodes of the graph are expected to have similar likelihood-ratio estimates when there is no change-point. We demonstrate the quality of our method on synthetic experiments and real-world applications.