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 …

All beauty, richness and harmony in the emergent dynamics of a complex system largely
depend on the specific way in which its elementary components interact. The last twenty-five …

Link prediction is a key problem for network-structured data. Link prediction heuristics use
some score functions, such as common neighbors and Katz index, to measure the likelihood …

Deep learning has been shown to be successful in a number of domains, ranging from
acoustics, images, to natural language processing. However, applying deep learning to the …

Signal propagation in complex networks drives epidemics, is responsible for information
going viral, promotes trust and facilitates moral behavior in social groups, enables the …

Understanding the circumstances that lead to pandemics is important for their prevention.
We analyzed the genomic diversity of severe acute respiratory syndrome coronavirus 2 …

We investigate the representation power of graph neural networks in the semi-supervised
node classification task under heterophily or low homophily, ie, in networks where …

Numerous important problems can be framed as learning from graph data. We propose a
framework for learning convolutional neural networks for arbitrary graphs. These graphs …

The mechanisms underlying ribonucleoprotein (RNP) granule assembly, including the basis
for establishing and maintaining RNP granules with distinct composition, are unknown. One …

Optical coherence tomography (OCT) was one of the biggest advances in ophthalmic
imaging. Building on that platform, OCT angiography (OCTA) provides depth resolved …