Complex networks are ubiquitous: a cell, the human brain, a group of people and the
Internet are all examples of interconnected many-body systems characterized by …

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 …

Believable proxies of human behavior can empower interactive applications ranging from
immersive environments to rehearsal spaces for interpersonal communication to prototy** …

Recently, transformer architectures for graphs emerged as an alternative to established
techniques for machine learning with graphs, such as (message-passing) graph neural …

Abstract Large Language Model (LLM) agents have been increasingly adopted as
simulation tools to model humans in social science and role-playing applications. However …

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 …

Abstract We present the Open Graph Benchmark (OGB), a diverse set of challenging and
realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine …

The novel coronavirus (COVID-19) pandemic spread globally from its outbreak in China in
early 2020, negatively affecting economies and industries on a global scale. In line with …

Large language models (LLMs) are capable of successfully performing many language
processing tasks zero-shot (without training data). If zero-shot LLMs can also reliably classify …

We develop a multi-risk SIR model (MR-SIR) where infection, hospitalization and fatality
rates vary between groups—in particular between the “young”,“the middle-aged” and the …