84 citations · 354 across the 25 of their papers we have counts for
8 papers · 1 filter
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…
A framework for second order eigenvector centralities and clustering coefficients
Francesca Arrigo, Desmond J. Higham, Francesco Tudisco
We propose and analyse a general tensor-based framework for incorporating second order features into network measures. This approach allows us to combine traditional pairwise links…
Generating large scale-free networks with the Chung-Lu random graph model
Dario Fasino, Arianna Tonetto, Francesco Tudisco
Random graph models are a recurring tool-of-the-trade for studying network structural properties and benchmarking community detection and other network algorithms. Moreover, they s…
Shifted and extrapolated power methods for tensor -eigenpairs
Stefano Cipolla, Michela Redivo-Zaglia, Francesco Tudisco
This work is concerned with the computation of -eigenvalues and eigenvectors of square tensors with modes. In the first part we propose two possible shifted variants of…
Total variation based community detection using a nonlinear optimization approach
Andrea Cristofari, Francesco Rinaldi, Francesco Tudisco
Maximizing the modularity of a network is a successful tool to identify an important community of nodes. However, this combinatorial optimization problem is known to be NP-complete…
Ergodicity coefficients for higher-order stochastic processes
Dario Fasino, Francesco Tudisco
The use of higher-order stochastic processes such as nonlinear Markov chains or vertex-reinforced random walks is significantly growing in recent years as they are much better at m…
Extrapolation Methods for fixed-point Multilinear PageRank computations
Stefano Cipolla, Michela Redivo-Zaglia, Francesco Tudisco
Nonnegative tensors arise very naturally in many applications that involve large and complex data flows. Due to the relatively small requirement in terms of memory storage and numb…
Spectral Clustering of Signed Graphs via Matrix Power Means
Pedro Mercado, Francesco Tudisco, Matthias Hein
Signed graphs encode positive (attractive) and negative (repulsive) relations between nodes. We extend spectral clustering to signed graphs via the one-parameter family of Signed P…