# On a Geometry of Interbrain Networks

arXiv: https://arxiv.org/abs/2509.10650 · HTML: https://mlsanity.com/paper/2509.10650/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 12 Sep 2025.

## Builds on (most cited first, 6 of 6)

- [Understanding over-squashing and bottlenecks on graphs via curvature](https://mlsanity.com/paper/2111.14522/) (2021)
- [Mitigating Over-Smoothing and Over-Squashing using Augmentations of Forman-Ricci Curvature](https://mlsanity.com/paper/2309.09384/) (2023)
- [A spectrum of routing strategies for brain networks](https://mlsanity.com/paper/1803.08541/) (2018)
- [Structure of activity in multiregion recurrent neural networks](https://mlsanity.com/paper/2402.12188/) (2024)
- [Towards principles of brain network organization and function](https://mlsanity.com/paper/2408.02640/) (2024)
- [Geometric Hyperscanning of Affect under Active Inference](https://mlsanity.com/paper/2506.08599/) (2025)

## Abstract (arXiv)

Effective analysis in neuroscience benefits significantly from robust conceptual frameworks. Traditional metrics of interbrain synchrony in social neuroscience typically depend on fixed, correlation-based approaches, restricting their explanatory capacity to descriptive observations. Inspired by the successful integration of geometric insights in network science, we propose leveraging discrete geometry to examine the dynamic reconfigurations in neural interactions during social exchanges. Unlike conventional synchrony approaches, our method interprets inter-brain connectivity changes through the evolving geometric structures of neural networks. This geometric framework is realized through a pipeline that identifies critical transitions in network connectivity using entropy metrics derived from curvature distributions. By doing so, we significantly enhance the capacity of hyperscanning methodologies to uncover underlying neural mechanisms in interactive social behavior.