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06/02/2023
Robust low-rank training via approximate orthonormal constraints
With the growth of model and data sizes, a broad effort has been made to...
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05/31/2023
Learning the Right Layers: a Data-Driven Layer-Aggregation Strategy for Semi-Supervised Learning on Multilayer Graphs
Clustering (or community detection) on multilayer graphs poses several a...
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05/30/2023
Rank-adaptive spectral pruning of convolutional layers during training
The computing cost and memory demand of deep learning pipelines have gro...
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03/09/2023
Optimizing network robustness via Krylov subspaces
We consider the problem of attaining either the maximal increase or redu...
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01/28/2023
Laplacian-based Semi-Supervised Learning in Multilayer Hypergraphs by Coordinate Descent
Graph Semi-Supervised learning is an important data analysis tool, where...
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01/09/2023
Quantifying the structural stability of simplicial homology
The homology groups of a simplicial complex reveal fundamental propertie...
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09/13/2022
Nonlinear Spectral Duality
Nonlinear eigenvalue problems for pairs of homogeneous convex functions ...
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05/26/2022
Low-rank lottery tickets: finding efficient low-rank neural networks via matrix differential equations
Neural networks have achieved tremendous success in a large variety of a...
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02/25/2022
Core-periphery detection in hypergraphs
Core-periphery detection is a key task in exploratory network analysis w...
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01/04/2022
Nodal domain count for the generalized graph p-Laplacian
Inspired by the linear Schrödinger operator, we consider a generalized p...
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11/18/2021
The self-consistent field iteration for p-spectral clustering
The self-consistent field (SCF) iteration, combined with its variants, i...
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05/30/2021
Hitting times for non-backtracking random walks
A non-backtracking random walk on a graph is a random walk where, at eac...
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03/27/2021
A nonlinear diffusion method for semi-supervised learning on hypergraphs
Hypergraphs are a common model for multiway relationships in data, and h...
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01/15/2021
Node and Edge Eigenvector Centrality for Hypergraphs
Network scientists have shown that there is great value in studying pair...
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06/08/2020
Nonlinear Higher-Order Label Spreading
Label spreading is a general technique for semi-supervised learning with...
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02/06/2020
Computing the norm of nonnegative matrices and the log-Sobolev constant of Markov chains
We analyze the global convergence of the power iterates for the computat...
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01/28/2020
Nonlocal PageRank
In this work we introduce and study a nonlocal version of the PageRank. ...
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10/30/2019
Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs
We study the task of semi-supervised learning on multilayer graphs by ta...
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10/28/2019
A framework for second order eigenvector centralities and clustering coefficients
We propose and analyse a general tensor-based framework for incorporatin...
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09/26/2019
Shifted and extrapolated power methods for tensor ℓ^p-eigenpairs
This work is concerned with the computation of ℓ^p-eigenvalues and eigen...
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07/10/2019
Higher-order ergodicity coefficients for stochastic tensors
Ergodicity coefficients for stochastic matrices provide valuable upper b...
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07/10/2019
Higher-order ergodicity coefficients
Ergodicity coefficients for stochastic matrices provide valuable upper b...
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05/15/2019
Spectral Clustering of Signed Graphs via Matrix Power Means
Signed graphs encode positive (attractive) and negative (repulsive) rela...
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09/21/2018
Multi-Dimensional, Multilayer, Nonlinear and Dynamic HITS
We introduce a ranking model for temporal multi-dimensional weighted and...
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03/01/2018
The Power Mean Laplacian for Multilayer Graph Clustering
Multilayer graphs encode different kind of interactions between the same...
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01/12/2018
A unifying Perron-Frobenius theorem for nonnegative tensors via multi-homogeneous maps
Inspired by the definition of symmetric decomposition, we introduce the ...
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09/15/2017
On the stability of network indices defined by means of matrix functions
Identifying important components in a network is one of the major goals ...
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08/18/2017
Community detection in networks via nonlinear modularity eigenvectors
Revealing a community structure in a network or dataset is a central pro...
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01/03/2017
Clustering Signed Networks with the Geometric Mean of Laplacians
Signed networks allow to model positive and negative relationships. We a...
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11/09/2015
