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gm

Clustering Signed Networks with the Geometric Mean of Laplacians

gssi-compmath

learning-the-right-layers-on-multilayer-graphs

This repository contains the codes of the paper "Learning the right layers: a data-driven layer-aggregation strategy for semi-supervised learning on multilayer graphs" by Sara Venturini, Andrea Cristofari, Francesco Rinaldi, Francesco Tudisco.

mds2020

Minisymposium on Learning from data on networks, part of the SIAM Conference on Mathematics of Data Science (MDS) 2020

minimed-alpha-v0

multi-dimensional-hits

Code used for the paper "Multi-dimensional HITS: An always computable ranking for temporal multi-layer directed networks", Arrigo, Tudisco, 2018

node-edge-hypergraph-centrality

node_layer_eigenvector_centrality

Code used for the paper "Node and layer eigenvector centralities for multiplex networks", Tudisco, Arrigo, Gautier, SIAM J. Appl. Math 2017

nonlinear-core-periphery

Code used for the paper "A nonlinear spectral method for core-periphery detection in networks" by F. Tudisco and D. J. Higham

pm

The Power Mean Laplacian for Multilayer Graph Clustering

pm_ssl

Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs

scalefreechunglu

Implementation of a fast Chung-Lu random graph generator

siam-nonlinear-pf-tutorial

spectralclcoeff