Understanding communication and information processing in nervous systems is a central
goal of neuroscience. Over the past two decades, advances in connectomics and network …

Recent advances in imaging and tracing technology provide increasingly detailed
reconstructions of brain connectomes. Concomitant analytic advances enable rigorous …

Learning representations of sets of nodes in a graph is crucial for applications ranging from
node-role discovery to link prediction and molecule classification. Graph Neural Networks …

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

The fundamental mathematical tools needed to understand machine learning include linear
algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability …

Networks are a fundamental tool for understanding and modeling complex systems in
physics, biology, neuroscience, engineering, and social science. Many networks are known …

The white matter architecture of the brain imparts a distinct signature on neuronal
coactivation patterns. Interregional projections promote synchrony among distant neuronal …

Network theory provides an intuitively appealing framework for studying relationships
among interconnected brain mechanisms and their relevance to behaviour. As the space of …

It is nearly 20 years since the concept of a small-world network was first quantitatively
defined, by a combination of high clustering and short path length; and about 10 years since …

The brain is characterized by heterogeneous patterns of structural connections supporting
unparalleled feats of cognition and a wide range of behaviours. New non-invasive imaging …