TY - JOUR
T1 - Laplacian-based Semi-Supervised Learning in Multilayer Hypergraphs by Coordinate Descent
AU - Venturini, Sara
AU - Cristofari, Andrea
AU - Rinaldi, Francesco
AU - Tudisco, Francesco
PY - 2023/10/27
Y1 - 2023/10/27
N2 - 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 formulation to multilayer hypergraphs. We solve the problem using different coordinate descent approaches and compare the results with the ones obtained by the classic gradient descent method. Experiments on synthetic and real-world datasets show the potential of using coordinate descent methods with suitable selection rules.
AB - 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 formulation to multilayer hypergraphs. We solve the problem using different coordinate descent approaches and compare the results with the ones obtained by the classic gradient descent method. Experiments on synthetic and real-world datasets show the potential of using coordinate descent methods with suitable selection rules.
U2 - 10.1016/j.ejco.2023.100079
DO - 10.1016/j.ejco.2023.100079
M3 - Article
SN - 2192-4406
VL - 11
JO - EURO Journal on Computational Optimization
JF - EURO Journal on Computational Optimization
M1 - 100079
ER -