Comparative Evaluation of Assumption Lean Community Detection Methods for Human Connectome Networks

Overview

Community detection provides a principled lens on mesoscale organization in functional brain networks, yet many widely used methods presume assortative structure and depend on arbitrary thresholding, which complicates the selection of the community count K. We conducted a systematic benchmark of three assumption lean approaches that operate directly on weighted functional connectivity matrices: the Weighted Stochastic Block Model, Spectral Clustering, and K-means. Performance was assessed on synthetic networks with known ground truth and on three neuroimaging cohorts spanning development, namely the Human Connectome Project, Washington University 120, and the Baby Connectome Project. We compared strategies for choosing K, including post hoc indices such as silhouette, Calinski–Harabasz, C index, modularity, variation of information, Normalized Mutual Information, and zRand, together with a likelihood-based criterion for the Weighted Stochastic Block Model that uses bootstrap confidence intervals for differences in log likelihood between successive values of K. In simulations all methods recovered stable partitions, but the post hoc indices favored incorrect values of K under weak signal and nonassortative mixing. In adult datasets the indices do not yield a unique optimum, whereas the likelihood-based criterion selects a parsimonious range centered near K=11, which is consistent with established sensory and association systems. In infants and toddlers, the same procedure supports a larger K around 15 and reveals developmentally distinct mesoscale architecture, including anterior and posterior subdivisions within default mode and fronto parietal systems. A consensus relabeling scheme based on Hungarian matching with Hamming distance further stabilizes solutions across runs and across values of K. Overall, threshold free weighted methods mitigate assortative bias and the likelihood-based comparison provides a reproducible path to selecting K.

Main Article

The full article is available at https://www.biorxiv.org/content/10.1101/2025.11.13.688333v1.

Repository Structure

BrainNetworks_WSBM_KM_SC/
│
├── atlases/                     # Brain atlases / region definitions
├── figures/                     # Figures generated by the code
│
├── main_functions/              # Scripts to run main pipelines and comparisons
│   ├── atlas_comparison.m
│   ├── consensus.m
│   ├── find_optimal_comm_assignment.m
│   ├── fit_methods.m
│   ├── inter_comparison.m
│   ├── intra_comparison.m
│   ├── simulation/              # Simulation subfolder
│       └── simulation_fc_matrix.m
│
├── support_functions/           # Utility and helper functions
│
├── visualization/               # Plotting and visualization scripts
│   ├── average_fcmatrix_plot.m
│   ├── consensus_surface_plot.m
│   └── surface_plot.m
│
└──  README.md                    # This file

Requirements

WSBM partition was generated using code from https://aaronclauset.github.io/wsbm/.

Reproducing the Results

1. Clone the repository:

git clone https://github.com/ayoushmanb/BrainNetworks_WSBM_KM_SC.git
cd BrainNetworks_WSBM_KM_SC

Citation

If you use this code or data, please cite:

@article {bhattacharya2025comparative,
	author = {Bhattacharya, Ayoushman and Chakraborty, Nilanjan and Wang, Xintian and Tu, Jiaxin (Cindy) and Dierker, Donna and Eck, Andy and Lahiri, Soumendra and Eggebrecht, Adam and Wheelock, Muriah D.},
	title = {{Comparative Evaluation of Assumption Lean Community Detection Methods for Human Connectome Networks}},
	elocation-id = {2025.11.13.688333},
	year = {2025},
	doi = {10.1101/2025.11.13.688333},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2025/11/14/2025.11.13.688333},
	eprint = {https://www.biorxiv.org/content/early/2025/11/14/2025.11.13.688333.full.pdf},
	journal = {bioRxiv}
}