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MAGNET

Models and Algorithms for Graph centrality grounded on Nonlinear Eigenvalues Techniques

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EC-Contrib. €

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Partnership

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Project "MAGNET" data sheet

The following table provides information about the project.

Coordinator
UNIVERSITY OF STRATHCLYDE 

Organization address
address: Richmond Street 16
city: GLASGOW
postcode: G1 1XQ
website: www.strath.ac.uk

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
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fax: n.a.

 Coordinator Country United Kingdom [UK]
 Project website http://personal.strath.ac.uk/f.tudisco/projects/magnet/
 Total cost 183˙454 €
 EC max contribution 183˙454 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2016
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2017
 Duration (year-month-day) from 2017-07-01   to  2019-06-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITY OF STRATHCLYDE UK (GLASGOW) coordinator 183˙454.00

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 Project objective

Models and Algorithms for Graph centrality grounded on Nonlinear Eigenvalues Techniques

The main objective of the project is to use nonlinear eigenvalue equations to model the importance of components in complex, large-scale and time-varying networks. Based on the nonlinear Perron-Frobenius theory, we will develop the theory to rigorously formalize the model from a mathematical viewpoint (existence, uniqueness, maximality). Based on the nonlinear spectral method for multi-homogeneous functions, we will develop numerical methods to compute the vector of nonlinear importances of the nodes. We will develop convergence analysis and quality guarantees for the algorithms and will use the methods to investigate the influence of nodes in large-scale networks arising from real-world applications. These theoretical and algorithmic advances will contribute the highly active research field of network centrality. Current tools for network centrality are based on linear models, and they can be shown to be inadequate in many realistic scenarios. The new methods developed here will have provably better performance. In addition to theoretical validation, the tools will be tested and refined on realistic data sets supplied by collaborators and external partners associated with the Institute for Future Cities at the University of Strathclyde.

The Researcher's expertise include nonlinear eigenvalue theory, graph theory and their use in machine learning. The Host and the research group at University of Strathclyde have strong internationally recognized experience in mathematics of network science and numerical mathematics. Thus the two-way transfer of knowledge will ensure to reach the research goals with highest quality and impact. Moreover, this will represent a great training opportunity for the researcher to jump-start his academic career.

 Publications

year authors and title journal last update
List of publications.
2018 Dario Fasino, Francesco Tudisco
The expected adjacency and modularity matrices in the degree corrected stochastic block model
published pages: 110-121, ISSN: 2300-7451, DOI: 10.1515/spma-2018-0010
Special Matrices 6/1 2020-01-22
2019 Gautier, Antoine; Tudisco, Francesco
The contractivity of cone-preserving multilinear mappings
published pages: , ISSN: , DOI:
Nonlinearity 2020-01-22
2019 Francesco Tudisco, Desmond J. Higham
A Nonlinear Spectral Method for Core--Periphery Detection in Networks
published pages: 269-292, ISSN: 2577-0187, DOI: 10.1137/18m1183558
SIAM Journal on Mathematics of Data Science 1/2 2020-01-22
2019 Gautier, Antoine; Tudisco, Francesco; Hein, Matthias
The Perron-Frobenius theorem for multi-homogeneous mappings
published pages: , ISSN: , DOI:
SIAM Journal on Matrix Analysis and Applications 2020-01-22
2018 Francesco Tudisco, Francesca Arrigo, Antoine Gautier
Node and Layer Eigenvector Centralities for Multiplex Networks
published pages: 853-876, ISSN: 0036-1399, DOI: 10.1137/17m1137668
SIAM Journal on Applied Mathematics 78/2 2020-01-22
2018 Francesco Tudisco, Pedro Mercado, Matthias Hein
Community Detection in Networks via Nonlinear Modularity Eigenvectors
published pages: 2393-2419, ISSN: 0036-1399, DOI: 10.1137/17m1144143
SIAM Journal on Applied Mathematics 78/5 2020-01-22
2018 Dario Fasino, Francesco Tudisco
A modularity based spectral method for simultaneous community and anti-community detection
published pages: 605-623, ISSN: 0024-3795, DOI: 10.1016/j.laa.2017.12.001
Linear Algebra and its Applications 542 2020-01-22
2018 Mercado, Pedro; Gautier, Antoine; Tudisco, Francesco; Hein, Matthias
The Power Mean Laplacian for Multilayer Graph Clustering
published pages: PMLR 84:1828-183, ISSN: , DOI:
Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics; Proceedings of Machine Learning Research 2020-01-22
2019 Francesco Tudisco, Desmond J. Higham
A fast and robust kernel optimization method for core--periphery detection in directed and weighted graphs
published pages: , ISSN: , DOI:
Applied Network Science 2020-01-22
2019 Pedro Mercado, Francesco Tudisco, Matthias Hein
Spectral Clustering of Signed Graphs via Matrix Power Means
published pages: , ISSN: , DOI:
Proceedings of the 36th International Conference on Machine Learning; Proceedings of Machine Learning Research 2020-01-22
2019 Gautier, Antoine; Tudisco, Francesco; Hein, Matthias
A unifying Perron-Frobenius theorem for nonnegative tensors via multi-homogeneous maps
published pages: , ISSN: , DOI:
SIAM Journal on Matrix Analysis and Applications 2020-01-22
2018 Stefano Pozza, Francesco Tudisco
On the Stability of Network Indices Defined by Means of Matrix Functions
published pages: 1521-1546, ISSN: 0895-4798, DOI: 10.1137/17m1133920
SIAM Journal on Matrix Analysis and Applications 39/4 2020-01-22

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