I first ran into energy-based learning when studying neural nets. Recently i found a few promising papers/examples that focus on energy based graph embedding.
I'm curious what the community thinks of this brand of learning? ------------ The guys @ Gephi published a nice overview of force/energy based embedding: webatlas.fr/tempshare/ForceAtlas2_Paper.pdf They have an implementation, and 2 cool video's on their site: https://gephi.org/2011/forceatlas2-the-new-version-of-our-home-brew-layout/ ------------ Andreas Noack has a simple, java implementation here: http://code.google.com/p/linloglayout/ ------------ This paper, from Inria, looks at distributed graph embedding: hal.inria.fr/inria-00495250/PDF/RR-7327.pdf And it looks like Gephi has a multithread implementation: https://code.launchpad.net/~mathieu-jacomy/gephi/forceatlas2-multithread ------------------------------------------------------------------------------ Cloud Services Checklist: Pricing and Packaging Optimization This white paper is intended to serve as a reference, checklist and point of discussion for anyone considering optimizing the pricing and packaging model of a cloud services business. Read Now! http://www.accelacomm.com/jaw/sfnl/114/51491232/ _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
