# Connectivity Estimation using Stochastic Graph Heat Modelling

arXiv: https://arxiv.org/abs/2606.29098 · HTML: https://mlsanity.com/paper/2606.29098/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 27 Jun 2026.

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

- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Learning heat diffusion graphs](https://mlsanity.com/paper/1611.01456/) (2016)
- [Graph Neural Network-based EEG Classification: A Survey](https://mlsanity.com/paper/2310.02152/) (2023)
- [Functional importance of noise in neuronal information processing](https://mlsanity.com/paper/1812.09897/) (2018)
- [Understanding Concepts in Graph Signal Processing for Neurophysiological Signal Analysis](https://mlsanity.com/paper/2312.03371/) (2023)
- [Stochastic Graph Heat Modelling for Diffusion-based Connectivity Retrieval](https://mlsanity.com/paper/2402.12785/) (2024)

## Abstract (arXiv)

A growing number of techniques leverage the spatial structures that underlie many real-world datasets. Despite these advances, the complementary task of estimating spatial structures and understanding their role within these techniques has often been overlooked. In neurophysiological data analysis specifically, numerous methods exist to estimate brain connectivity, but most are not explicitly model-based, dynamic, multivariate, or directed. To address these limitations, we previously introduced noise-driven heat modelling on graphs for neurophysiological connectivity estimation. In this study, we extend this framework by relaxing earlier noise assumptions and adding regularisation to improve robustness. We also develop a simulation procedure to characterise and evaluate our technique in a controlled setting. Finally, we demonstrate that the technique is able to capture meaningful spatial structure across two experiments, each using two real-world datasets. The explicit model formulation of our connectivity estimator has the potential to improve the interpretability of graph-based techniques across a wide range of applications. The code implementing our method is available at https://github.com/sgoerttler/Heat_Connectivity.