# Hypothesis testing for community structure in temporal networks using e-values

arXiv: https://arxiv.org/abs/2507.23034 · HTML: https://mlsanity.com/paper/2507.23034/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2025.

## Built on by (most cited first, 2 of 2)

- [Statistically Valid Post-Training Hyperparameter Selection: From Tuning to Guarantees](https://mlsanity.com/paper/2606.25601/) (2026)
- [Post-Selection Distributional Model Evaluation](https://mlsanity.com/paper/2603.23055/) (2026)

## Abstract (arXiv)

Community structure in networks naturally arises in various applications. But while the topic has received significant attention for static networks, the literature on community structure in temporally evolving networks is more scarce. In particular, there are currently no statistical methods available to test for the presence of community structure in a sequence of networks evolving over time. In this work, we propose a simple yet powerful test using e-values, an alternative to p-values that is more flexible in certain ways. Specifically, an e-value framework retains valid testing properties even after combining dependent information, a relevant feature in the context of testing temporal networks. We apply the proposed test to synthetic and real-world networks, demonstrating various features inherited from the e-value formulation and exposing some of the inherent difficulties of testing on temporal networks.