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

ABSTRACT: 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,

SUBMITTER: Bhattacharya A 

PROVIDER: S-EPMC12642645 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature