Nilearn is a Python module for fast and easy statistical learning on NeuroImaging data. It leverages the scikit-learn Python toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis.

Label propagation is a popular approach to semi-supervised learning in which nodes in a graph represent features or instances to be classified and the labels for a classification task are pushed around the graph from nodes that have initial label assignments to their neighbors and beyond.

Information

• Website: http://nilearn.github.io/
• GitHub: https://github.com/nilearn/nilearn
• Documentation: http://nilearn.github.io/user_guide.html
• Examples: http://nilearn.github.io/auto_examples/index.html