Network-based modeling of complex systems and data using the language of graphs has
become an essential topic across a range of different disciplines. Arguably, this graph-based …

Neuronal signalling and communication underpin virtually all aspects of brain activity and
function. Network science approaches to modelling and analysing the dynamics of …

Community detection, the decomposition of a graph into essential building blocks, has been
a core research topic in network science over the past years. Since a precise notion of what …

A complex system can be represented and analyzed as a network, where nodes represent
the units of the network and edges represent connections between those units. For example …

Convolutional neural networks have achieved impressive results in areas such as computer
vision tasks. Recently, more complex architectures have been designed that add additional …

We present a graph-theoretical approach to data clustering, which combines the creation of
a graph from the data with Markov Stability, a multiscale community detection framework. We …

This paper focuses on devising graph signal processing tools for the treatment of data
defined on the edges of a graph. We first show that conventional tools from graph signal …

Signaling in brain networks unfolds over multiple topological scales. Areas may exchange
information over local circuits, encompassing direct neighbours and areas with similar …

Discovering the community structure within networks is of significance with respect to many
realistic applications, like recommendation systems and cyberattack detection. In this study …

This chapter unfolds different aims underpinning community detection–in a relaxed form that
includes assortative as well as disassortative group structures with dense and sparse …