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FunctionalSubgraph: An ML tool for dynamic graph analysis.

Project description

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


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