Generalized matrix means for semisupervised learning with multilayer graphs
Advances in Neural Information Processing Systems (NeurIPS), 2019
We study the task of semi-supervised learning on multilayer graphs by taking into account both labeled and unlabeled observations together with the information encoded by each individual graph layer. We propose a regularizer based on the generalized matrix mean, which is a one-parameter family of matrix means that …
Spectral Clustering of Signed Graphs via Matrix Power Means
International Conference on Machine Learning (ICML), pp. 4526–4536, 2019
Signed graphs encode positive (attractive) and negative (repulsive) relations between nodes. We extend spectral clustering to signed graphs via the one-parameter family of Signed Power Mean Laplacians, defined as the matrix power mean of normalized standard and signless Laplacians of positive and negative edges. We …
The Power Mean Laplacian for Multilayer Graph Clustering
International Conference on Artificial Intelligence and Statistics (AISTATS), vol. 84, pp. 1828–1838, 2018
Multilayer graphs encode different kind of interactions between the same set of entities. When one wants to cluster such a multilayer graph, the natural question arises how one should merge the information form different layers. We introduce in this paper a one-parameter family of matrix power means for merging the …
Clustering Signed Networks with the Geometric Mean of Laplacians
Advances in Neural Information Processing Systems (NeurIPS), vol. 29, 2016
Signed networks allow to model positive and negative relationships. We analyze existing extensions of spectral clustering to signed networks. It turns out that existing approaches do not recover the ground truth clustering in several situations where either the positive or the negative network structures contain no …