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Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs
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powerMeanLaplacian
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README.md
SSL_multilayer_graphs_with_power_mean_laplacian.m
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multilayer_V1_PM.m
multilayer_V1_PM_CMN.m
multilayer_V1_PM_rebalanced.m
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realworld_experiments.m
run_everything.m
submit_run_everything.txt

README.md

Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs

MATLAB implementation of the paper:

P. Mercado, F. Tudisco, and M. Hein, Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs. In NeurIPS 2019.

Content:

  • example.m : contains an easy example showing how to use the code

  • realworld_experiments.m : runs experiments on real datasets contained in our paper (Section 6)

  • run_everything.m : runs experiments contained in our paper

Usage:

Let Wcell be a cell with the adjacency matrices of each layer , p the power of the power mean Laplacian, y an array with the class of labeled nodes (zero denotes node is unlabeled). Classes through the power mean Laplacian L_p regularizer are computed via

y_hat = SSL_multilayer_graphs_with_power_mean_laplacian(Wcell, p, y);

Quick Overview:

Citation:

@article{mercadoNeurIPS2019,
  title = 	 {Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs},
  author = 	 {Mercado, Pedro and Tudisco, Francesco and Hein, Matthias},
  conference = 	 {NeurIPS},
  year = 	 {2019},
}

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