Graph Data Processing

Pierre Vandergheynst

The goal of this course is to provide a broad introduction to effective algorithms in data science and network analysis. A major effort will be given to show that existing data analysis techniques can be defined and enhanced on graphs. Graphs encode complex structures like cerebral connection, stock exchange, and social network. Strong mathematical tools have been developed based on linear and non-linear graph spectral harmonic analysis to advance the standard data analysis algorithms. Main topics of the course are networks, unsupervised and supervised learning, recommendation, visualization.

Mots-clés

Graphs, learning, data science

Prérequis

Signal processing, convex optimisation, automatic learning

Contenu

Nous aborderons les méthodes spectrales, en particulier spectral clustering et laplacian eigenmaps, ainsi que les techniques les plus récentes permettant l'apprentissage supervisé et semi-supervisé de données

Compétences acquises

Etre capable d'appliquer les algorithmes les plus importants en science des données.