abct
abct (abct.rubinovlab.net) is a MATLAB and Python toolbox for unsupervised learning, network science, and imaging/network neuroscience.
The toolbox includes three variants of global residualization, the Loyvain and co-Loyvain methods (for k-means, k-modularity, or spectral clustering of data or network inputs), as well as binary and weighted canonical and co-neighbor components, and m-umap embeddings. It also includes computation of degree centralities (first, second, and residual), dispersion centralities (squared coefficient of variation, k-participation coefficient), and network shrinkage.
See this reference for more details: Rubinov M. Unifying equivalences across unsupervised learning, network science, and imaging/network neuroscience. arXiv, 2025, doi:10.48550/arXiv.2508.10045.
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Interactive examples
The examples illustrate the main analyses in the above study, and can be run interactively as online Jupyter notebooks in Google Colab (You need a Google account). To run in Colab, click the Open in Colab button at the top of the page of each example.
Our example brain-imaging data come from the Human Connectome Project, a large brain-imaging resource.
NB: For illustrative ease, some example analyses are simplified versions of analyses in the original study.
Contact
Mika Rubinov: mika.rubinov at vanderbilt.edu