# Change Point Detection in Correlation Networks

arXiv: https://arxiv.org/abs/1410.0761 · HTML: https://mlsanity.com/paper/1410.0761/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2014.

## Built on by (most cited first, 6 of 6)

- [Neural network-based CUSUM for online change-point detection](https://mlsanity.com/paper/2210.17312/) (2022)
- [Graph similarity learning for change-point detection in dynamic networks](https://mlsanity.com/paper/2203.15470/) (2022)
- [Incremental Learning with Concept Drift Detection and Prototype-based Embeddings for Graph Stream Classification](https://mlsanity.com/paper/2404.02572/) (2024)
- [Online Heavy-tailed Change-point detection](https://mlsanity.com/paper/2306.09548/) (2023)
- [RIO-CPD: A Riemannian Geometric Method for Correlation-aware Online Change Point Detection](https://mlsanity.com/paper/2407.09698/) (2024)
- [Conditional Entropy of Heat Diffusion on Temporal Networks](https://mlsanity.com/paper/2605.21514/) (2026)

## Abstract (arXiv)

Many systems of interacting elements can be conceptualized as networks, where network nodes represent the elements and network ties represent interactions between the elements. In systems where the underlying network evolves in time, it is useful to determine the points in time where the network structure changes significantly as these may correspond also to functional change points. We propose a method for detecting these change points in correlation networks that, unlike previous change point detection methods designed for time series data, requires no distributional assumptions. We investigate the difficulty of change point detection near the boundaries of data in correlation networks and demonstrate the power of our method and a competing method through simulation. We also show the generalizable nature of our method by applying it to stock price data as well as fMRI data.