Laplacian-based Semi-Supervised Learning in Multilayer Hypergraphs by Coordinate Descent
EURO Journal on Computational Optimization, 2023
Graph Semi-Supervised learning is an important data analysis tool, where given a graph and a set of labeled nodes, the aim is to infer the labels to the remaining unlabeled nodes. In this paper, we start by considering an optimization-based formulation of the problem for an undirected graph, and then we extend this …
Learning the right layers: a data-driven layer-aggregation strategy for semi-supervised learning on multilayer graphs
International Conference on Machine Learning (ICML), 2023
Clustering (or community detection) on multilayer graphs poses several additional complications with respect to standard graphs as different layers may be characterized by different structures and types of information. One of the major challenges is to establish the extent to which each layer contributes to the cluster …
Nonlinear Feature Diffusion on Hypergraphs
International Conference on Machine Learning (ICML), 2022
Hypergraphs are a common model for multiway relationships in data, and hypergraph semi-supervised learning is the problem of assigning labels to all nodes in a hypergraph, given labels on just a few nodes. Diffusions and label spreading are classical techniques for semi-supervised learning in the graph setting, and …
Nonlinear Higher-Order Label Spreading
The Web Conference (WWW), pp. 2402–2413, 2021
Label spreading is a general technique for semi-supervised learning with point cloud or network data, which can be interpreted as a diffusion of labels on a graph. While there are many variants of label spreading, nearly all of them are linear models, where the incoming information to a node is a weighted sum of …
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 …