# Detecting change points in the large-scale structure of evolving networks

arXiv: https://arxiv.org/abs/1403.0989 · HTML: https://mlsanity.com/paper/1403.0989/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2014.

## Built on by (most cited first, 13 of 13)

- [Laplacian Change Point Detection for Single and Multi-view Dynamic Graphs](https://mlsanity.com/paper/2302.01204/) (2023)
- [Online Change Point Detection for Weighted and Directed Random Dot Product Graphs](https://mlsanity.com/paper/2201.11222/) (2022)
- [Anomaly Detection in Multiplex Dynamic Networks: from Blockchain Security to Brain Disease Prediction](https://mlsanity.com/paper/2211.08378/) (2022)
- [Neural network-based CUSUM for online change-point detection](https://mlsanity.com/paper/2210.17312/) (2022)
- [Event Detection on Dynamic Graphs](https://mlsanity.com/paper/2110.12148/) (2021)
- [Graph similarity learning for change-point detection in dynamic networks](https://mlsanity.com/paper/2203.15470/) (2022)
- [Anomaly Detection in Graph Structured Data: A Survey](https://mlsanity.com/paper/2405.06172/) (2024)
- [HLSAD: Hodge Laplacian-based Simplicial Anomaly Detection](https://mlsanity.com/paper/2505.24534/) (2025)
- [Latent Evolution Model for Change Point Detection in Time-varying Networks](https://mlsanity.com/paper/2212.08818/) (2022)
- [Fast and Attributed Change Detection on Dynamic Graphs with Density of States](https://mlsanity.com/paper/2305.08750/) (2023)
- [Incremental Learning with Concept Drift Detection and Prototype-based Embeddings for Graph Stream Classification](https://mlsanity.com/paper/2404.02572/) (2024)
- [Balancing Summarization and Change Detection in Graph Streams](https://mlsanity.com/paper/2311.18694/) (2023)
- [Reproduction of scan B-statistic for kernel change-point detection algorithm](https://mlsanity.com/paper/2408.13146/) (2024)

## Abstract (arXiv)

Interactions among people or objects are often dynamic in nature and can be represented as a sequence of networks, each providing a snapshot of the interactions over a brief period of time. An important task in analyzing such evolving networks is change-point detection, in which we both identify the times at which the large-scale pattern of interactions changes fundamentally and quantify how large and what kind of change occurred. Here, we formalize for the first time the network change-point detection problem within an online probabilistic learning framework and introduce a method that can reliably solve it. This method combines a generalized hierarchical random graph model with a Bayesian hypothesis test to quantitatively determine if, when, and precisely how a change point has occurred. We analyze the detectability of our method using synthetic data with known change points of different types and magnitudes, and show that this method is more accurate than several previously used alternatives. Applied to two high-resolution evolving social networks, this method identifies a sequence of change points that align with known external "shocks" to these networks.