Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …

As ChatGPT goes viral, generative AI (AIGC, aka AI-generated content) has made headlines
everywhere because of its ability to analyze and create text, images, and beyond. With such …

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 …

Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent
years owing to their capability to model and learn from graph-structured data. Such an ability …

Modeling and generating graphs is fundamental for studying networks in biology,
engineering, and social sciences. However, modeling complex distributions over graphs …

Graph neural networks (GNNs) are a popular class of machine learning models that have
been successfully applied to a range of problems. Their major advantage lies in their ability …

We propose a new family of efficient and expressive deep generative models of graphs,
called Graph Recurrent Attention Networks (GRANs). Our model generates graphs one …

Graphs are fundamental data structures which concisely capture the relational structure in
many important real-world domains, such as knowledge graphs, physical and social …

Graphs are ubiquitous data structures for representing interactions between entities. With an
emphasis on applications in chemistry, we explore the task of learning to generate graphs …

Large networks are becoming a widely used abstraction for studying complex systems in a
broad set of disciplines, ranging from social-network analysis to molecular biology and …