Laplacian-based Semi-Supervised Learning in Multilayer Hypergraphs by Coordinate Descent
This work addresses semi-supervised learning for data analysis in complex multilayer hypergraph structures, representing an incremental improvement over existing methods.
The paper tackles the problem of semi-supervised learning on multilayer hypergraphs by extending an optimization-based formulation from undirected graphs and solving it with coordinate descent methods, showing their potential through experiments on synthetic and real-world datasets.
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.