Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs

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},
}