The brain is an assembly of neuronal populations interconnected by structural pathways.
Brain activity is expressed on and constrained by this substrate. Therefore, statistical …

The application of graph theory to model the complex structure and function of the brain has
shed new light on its organization, prompting the emergence of network neuroscience …

Graph Signal Processing has become a very useful framework for signal operations and
representations defined on irregular domains. Exploiting transformations that are defined on …

Network analyses are now considered fundamental for understanding brain function.
Nonetheless neuroimaging characterisations of connectivity are just emerging in clinical …

Resting-state functional magnetic resonance imaging (fMRI) is a well-established
methodology to probe brain organization. Previous work has demonstrated that the …

An extensive range of problems in machine learning deals with data structured over
networks/graphs. The examples vary from drug discovery to traffic forecasting, social …

Functional magnetic resonance imaging (fMRI) is providing large amounts of data about
brain function. Measuring correlations between spontaneous activity time courses from …

We propose a graph spectral representation of time series data that 1) is parsimoniously
encoded to user-demanded resolution; 2) is unsupervised and performant in data …

Resumé Un large éventail de problèmes d'apprentissage automatique traite des données
structurées sur des réseaux/graphes. Les exemples vont de la découverte de médicaments …

Graphs are a fundamental tool in Computer Science in a variety of areas such as Artificial
Intelligence, Machine Learning, Networking, Signal Processing, Brain Map**, Social …