# Sequential change-point detection for mutually exciting point processes over networks

arXiv: https://arxiv.org/abs/2102.05724 · HTML: https://mlsanity.com/paper/2102.05724/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 35 references that are in the map.
- First posted on arXiv 10 Feb 2021.

## Builds on (most cited first, 5 of 5)

- [A Review of Self-Exciting Spatio-Temporal Point Processes and Their Applications](https://mlsanity.com/paper/1708.02647/) (2017)
- [Uncovering Causality from Multivariate Hawkes Integrated Cumulants](https://mlsanity.com/paper/1607.06333/) (2016)
- [SIR-Hawkes: Linking Epidemic Models and Hawkes Processes to Model Diffusions in Finite Populations](https://mlsanity.com/paper/1711.01679/) (2017)
- [Uncertainty Quantification for Inferring Hawkes Networks](https://mlsanity.com/paper/2006.07506/) (2020)
- [Detecting Abrupt Changes in High-Dimensional Self-Exciting Poisson Processes](https://mlsanity.com/paper/2006.03572/) (2020)

## Built on by (most cited first, 7 of 7)

- [Online Detection Of Supply Chain Network Disruptions Using Sequential Change-Point Detection for Hawkes Processes](https://mlsanity.com/paper/2211.12091/) (2022)
- [Conjugate Bayesian Two-step Change Point Detection for Hawkes Process](https://mlsanity.com/paper/2409.17591/) (2024)
- [PERCEPT: a new online change-point detection method using topological data analysis](https://mlsanity.com/paper/2203.04246/) (2022)
- [Score-Based Change-Point Detection and Region Localization for Spatio-Temporal Point Processes](https://mlsanity.com/paper/2602.04798/) (2026)
- [Detecting Structural Shifts in Multivariate Hawkes Processes with Fr'echet Statistics](https://mlsanity.com/paper/2308.06769/) (2023)
- [Analyzing Complex Systems with Cascades Using Continuous-Time Bayesian Networks](https://mlsanity.com/paper/2308.10606/) (2023)
- [Learning under Commission and Omission Event Outliers](https://mlsanity.com/paper/2501.13599/) (2025)

## Abstract (arXiv)

We present a new CUSUM procedure for sequentially detecting change-point in the self and mutual exciting processes, a.k.a. Hawkes networks using discrete events data. Hawkes networks have become a popular model for statistics and machine learning due to their capability in modeling irregularly observed data where the timing between events carries a lot of information. The problem of detecting abrupt changes in Hawkes networks arises from various applications, including neuronal imaging, sensor network, and social network monitoring. Despite this, there has not been a computationally and memory-efficient online algorithm for detecting such changes from sequential data. We present an efficient online recursive implementation of the CUSUM statistic for Hawkes processes, both decentralized and memory-efficient, and establish the theoretical properties of this new CUSUM procedure. We then show that the proposed CUSUM method achieves better performance than existing methods, including the Shewhart procedure based on count data, the generalized likelihood ratio (GLR) in the existing literature, and the standard score statistic. We demonstrate this via a simulated example and an application to population code change-detection in neuronal networks.