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

The constituents of many complex systems are characterized by non-trivial connectivity
patterns and dynamical processes that are well captured by network models. However, most …

Recent decades have seen a rise in the use of physics methods to study different societal
phenomena. This development has been due to physicists venturing outside of their …

The actions and policies enacted by today's youth hold profound implications for future
generations, underscoring their pivotal role in advocating for climate issues. Younger …

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 …

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 …

Graph convolution is the core of most Graph Neural Networks (GNNs) and usually
approximated by message passing between direct (one-hop) neighbors. In this work, we …

Graph Convolutional Networks (GCNs) are leading methods for learning graph
representations. However, without specially designed architectures, the performance of …

In the wake of the COVID-19 pandemic many countries implemented containment measures
to reduce disease transmission. Studies using digital data sources show that the mobility of …