# Multiscale Community-Based Fingerprinting of Signed Functional Networks

arXiv: https://arxiv.org/abs/2608.27483 · HTML: https://mlsanity.com/paper/2608.27483/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 44 references that are in the map.
- First posted on arXiv 25 Aug 2026.

## Builds on (most cited first, 4 of 4)

- [Community detection in networks: A user guide](https://mlsanity.com/paper/1608.00163/) (2016)
- [Multi-scale brain networks](https://mlsanity.com/paper/1608.08828/) (2016)
- [GEFF: Graph Embedding for Functional Fingerprinting](https://mlsanity.com/paper/2001.06605/) (2020)
- [High-Accuracy Machine Learning Techniques for Functional Connectome Fingerprinting and Cognitive State Decoding](https://mlsanity.com/paper/2211.07507/) (2022)

## Abstract (arXiv)

Objective: Recent studies demonstrate that functional connectomes contain subject-specific signatures, or textitfingerprints, that can identify individuals across repeated sessions and tasks. Existing methods mostly rely on edge-level features that are sensitive to noise, difficult to interpret, and limited in their ability to generalize across tasks and datasets. Methods: We propose a multiscale community-based functional connectome fingerprinting framework that characterizes each individual by the mesoscale structure of their functional networks. We introduce a signed multilayer community detection framework that incorporates both correlated and anti-correlated brain activity to identify subject-specific community structures across tasks and sessions. Graph-theoretic metrics are then computed from the resulting joint community structures to derive low-dimensional community-level fingerprint representations. Results: The proposed framework is evaluated on 810 healthy control subjects from the Human Connectome Project (HCP). The results show that community-based fingerprints provide a reliable and interpretable substrate for individualized brain characterization across sessions and tasks. Conclusion: Mesoscale community structure provides meaningful and discriminative subject-specific fingerprints. Significance: The proposed framework offers a promising foundation for precision neuroimaging and personalized neuroscience applications.