activity
20162022
most citedSpectral Clustering of Signed Graphs via Matrix Power Means

13 citations · 27 across the 4 of their papers we have counts for

collaborators

15 papers

math.OC20221 cited

Nonlinear Spectral Duality

Francesco Tudisco, Dong Zhang

Nonlinear eigenvalue problems for pairs of homogeneous convex functions are particular nonlinear constrained optimization problems that arise in a variety of settings, including gr…

cs.SI2021

Node and Edge Nonlinear Eigenvector Centrality for Hypergraphs

Francesco Tudisco, Desmond J. Higham

Network scientists have shown that there is great value in studying pairwise interactions between components in a system. From a linear algebra point of view, this involves definin…

cs.LG2020

Nonlinear Higher-Order Label Spreading

Francesco Tudisco, Austin R. Benson, Konstantin Prokopchik

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…

math.NA20205 cited

Computing the norm of nonnegative matrices and the log-Sobolev constant of Markov chains

Antoine Gautier, Matthias Hein, Francesco Tudisco

We analyze the global convergence of the power iterates for the computation of a general mixed-subordinate matrix norm. We prove a new global convergence theorem for a class of ent…

cs.SI2020

Nonlocal PageRank

Stefano Cipolla, Fabio Durastante, Francesco Tudisco

In this work we introduce and study a nonlocal version of the PageRank. In our approach, the random walker explores the graph using longer excursions than just moving between neigh…

cs.LG20198 cited

Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs

Pedro Mercado, Francesco Tudisco, Matthias Hein

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 indi…