Francesco Tudisco

Associate Professor (Reader) in Machine Learning

School of Mathematics, The University of Edinburgh
The Maxwell Institute for Mathematical Sciences
School of Mathematics, Gran Sasso Science Institute JCMB, King’s Buildings, Edinburgh EH93FD UK
email: f dot tudisco at

Laplacian-based Semi-Supervised Learning in Multilayer Hypergraphs by Coordinate Descent

Sara Venturini, Andrea Cristofari, Francesco Rinaldi, Francesco Tudisco,
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 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.

Please cite this paper as:

  title={Laplacian-based Semi-Supervised Learning in Multilayer Hypergraphs by Coordinate Descent},
  author={Venturini, S. and Cristofari, A. and Rinaldi, F. and Tudisco, F.},
  journal={EURO Journal on Computational Optimization},

Links: arxiv doi code

Keywords: semi-supervised learning classification label propagation clustering community detection hypergraph networks higher-order networks