Gathering events, eg, going to gyms and meetings, are ubiquitous and crucial in the
spreading phenomena, which induce higher-order interactions, and thus can be described …

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 …

Abstract We present the Temporal Graph Benchmark (TGB), a collection of challenging and
diverse benchmark datasets for realistic, reproducible, and robust evaluation of machine …

Diffuse gliomas, particularly glioblastomas, are incurable brain tumours. They are
characterized by networks of interconnected brain tumour cells that communicate via Ca2+ …

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 …

With the widespread popularization of social network platforms, user-generated content and
other social network data are growing rapidly. It is difficult for social users to select interested …

Predicting molecular properties with data-driven methods has drawn much attention in
recent years. Particularly, Graph Neural Networks (GNNs) have demonstrated remarkable …

Deep learning methods are achieving ever-increasing performance on many artificial
intelligence tasks. A major limitation of deep models is that they are not amenable to …

Social relations are often used to improve recommendation quality when user-item
interaction data is sparse in recommender systems. Most existing social recommendation …

Graph representation learning has emerged as a powerful technique for addressing real-
world problems. Various downstream graph learning tasks have benefited from its recent …