Functional Subgraph

A machine learning toolbox for the analysis of dynamic graphs.

Functional Subgraph implements non-negative matrix factorization to decompose time-varying, dynamic graphs into a composite set of parts-based, additive subgraphs.

Quick-Start

Non-Negative Matrix Factorization for dynamic graphs, such that:

A ~= WH Constraints:

A, W, H >= 0 L2-Regularization on W L1-Sparsity on H

Implementation is based on :

1. Jingu Kim, Yunlong He, and Haesun Park. Algorithms for Nonnegative

   Matrix and Tensor Factorizations: A Unified View Based on Block Coordinate Descent Framework. Journal of Global Optimization, 58(2), pp. 285-319, 2014.

2. Jingu Kim and Haesun Park. Fast Nonnegative Matrix Factorization:

   An Active-set-like Method And Comparisons. SIAM Journal on Scientific Computing (SISC), 33(6), pp. 3261-3281, 2011.

Modified from: https://github.com/kimjingu/nonnegfac-python