Skip to main content
Join the official 2019 Python Developers SurveyStart the survey!

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

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

A ~= WH
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:

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for FunctionalSubgraph, version 1.0.0.post10
Filename, size File type Python version Upload date Hashes
Filename, size FunctionalSubgraph-1.0.0.post10-py2.py3-none-any.whl (17.7 kB) File type Wheel Python version py2.py3 Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page