# Modularity maximization as a flexible and generic framework for brain network exploratory analysis

arXiv: https://arxiv.org/abs/2106.15428 · HTML: https://mlsanity.com/paper/2106.15428/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2021.

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

- [DBGDGM: Dynamic Brain Graph Deep Generative Model](https://mlsanity.com/paper/2301.11408/) (2023)
- [CoSeNet: A Novel Approach for Optimal Segmentation of Correlation Matrices](https://mlsanity.com/paper/2512.21000/) (2025)

## Abstract (arXiv)

The modular structure of brain networks supports specialized information processing, complex dynamics, and cost-efficient spatial embedding. Inter-individual variation in modular structure has been linked to differences in performance, disease, and development. There exist many data-driven methods for detecting and comparing modular structure, the most popular of which is modularity maximization. Although modularity maximization is a general framework that can be modified and reparamaterized to address domain-specific research questions, its application to neuroscientific datasets has, thus far, been narrow. Here, we highlight several strategies in which the ``out-of-the-box'' version of modularity maximization can be extended to address questions specific to neuroscience. First, we present approaches for detecting ``space-independent'' modules and for applying modularity maximization to signed matrices. Next, we show that the modularity maximization frame is well-suited for detecting task- and condition-specific modules. Finally, we highlight the role of multi-layer models in detecting and tracking modules across time, tasks, subjects, and modalities. In summary, modularity maximization is a flexible and general framework that can be adapted to detect modular structure resulting from a wide range of hypotheses. This article highlights opens multiple frontiers for future research and applications.