The complexity of many biological, social and technological systems stems from the richness
of the interactions among their units. Over the past decades, a variety of complex systems …

Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent
years owing to their capability to model and learn from graph-structured data. Such an ability …

Data augmentation has been widely used to improve generalizability of machine learning
models. However, comparatively little work studies data augmentation for graphs. This is …

Data augmentation has recently seen increased interest in graph machine learning given its
demonstrated ability to improve model performance and generalization by added training …

This paper summarizes a vision of the challenges facing the so-called “Industry of the
Future” as studied by the research community of the IFAC Coordinating Committee 5 on …

Time series analysis has proven to be a powerful method to characterize several
phenomena in biology, neuroscience and economics, and to understand some of their …

While advances in computing resources have made processing enormous amounts of data
possible, human ability to identify patterns in such data has not scaled accordingly. Efficient …

Driven by growing interest across the sciences, a large number of empirical studies have
been conducted in recent years of the structure of networks ranging from the Internet and the …

Multi-modality based classification methods are superior to the single modality based
approaches for the automatic diagnosis of the Alzheimer's disease (AD) and mild cognitive …

Most empirical studies of complex networks do not return direct, error-free measurements of
network structure. Instead, they typically rely on indirect measurements that are often error …