In recent years, network analysis has been applied to identify and analyse patterns of
statistical association in multivariate psychological data. In these approaches, network …

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 field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Brains contain networks of interconnected neurons and so knowing the network architecture
is essential for understanding brain function. We therefore mapped the synaptic-resolution …

Environmental stress is increasing worldwide, yet we lack a clear picture of how stress
disrupts the stability of microbial communities and the ecosystem services they provide …

Recently, contrastive learning (CL) has emerged as a successful method for unsupervised
graph representation learning. Most graph CL methods first perform stochastic augmentation …

Does Facebook enable ideological segregation in political news consumption? We
analyzed exposure to news during the US 2020 election using aggregated data for 208 …

Despite recent progress in Graph Neural Networks (GNNs), explaining predictions made by
GNNs remains a challenging open problem. The leading method mainly addresses the local …

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 …

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 …