# Optimal network online change point localisation

@article{Yu2021OptimalNO, title={Optimal network online change point localisation}, author={Yi Yu and Oscar Hernan Madrid Padilla and Daren Wang and Alessandro Rinaldo}, journal={ArXiv}, year={2021}, volume={abs/2101.05477} }

We study the problem of online network change point detection. In this setting, a collection of independent Bernoulli networks is collected sequentially, and the underlying distributions change when a change point occurs. The goal is to detect the change point as quickly as possible, if it exists, subject to a constraint on the number or probability of false alarms. In this paper, on the detection delay, we establish a minimax lower bound and two upper bounds based on NP-hard algorithms and…

## 6 Citations

Locally private online change point detection

- Computer Science, MathematicsNeurIPS
- 2021

This work provides algorithms which respect the LDP constraint, which control the false alarm probability, and which detect changes with a minimal (minimax rate-optimal) delay, and presents the optimal rate in the benchmark, non-private setting.

Change-Point Detection in Dynamic Networks with Missing Links

- Computer Science
- 2021

The proposed test for change-point detection at a temporal sequence of partially observed networks is based on the Matrix CUSUM test statistic and allows growing size of networks and is minimax optimal and robust to missing links.

Graph similarity learning for change-point detection in dynamic networks

- Computer ScienceArXiv
- 2022

This work designs a method to perform online network change-point detection that can adapt to the network domain and localise changes with no delay and requires a shorter data history to detect changes than most existing state-of-the-art baselines.

Online Change Point Detection for Random Dot Product Graphs

- Computer Science, Mathematics2021 55th Asilomar Conference on Signals, Systems, and Computers
- 2021

This paper considers the cumulative sum of a judicious monitoring function, which quantifies the discrepancy between the streaming graph observations and the nominal model, and develops a lightweight online CPD algorithm, with a proven capability to flag distribution shifts in the arriving graphs.

Online Change Point Detection for Weighted and Directed Random Dot Product Graphs

- Computer ScienceIEEE Transactions on Signal and Information Processing over Networks
- 2022

This work offers an open-source implementation of the novel online CPD algorithm for weighted and direct graphs, whose effectiveness and efficiency are demonstrated via (reproducible) synthetic and real network data experiments.

Inference in high-dimensional online changepoint detection

- Mathematics, Computer Science
- 2021

An online algorithm is proposed that produces an interval with guaranteed nominal coverage, and whose length is, with high probability, of the same order as the average detection delay, up to a logarithmic factor.

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