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 …

Embedding static graphs in low-dimensional vector spaces plays a key role in network
analytics and inference, supporting applications like node classification, link prediction, and …

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 …

We present PyTorch Geometric Temporal, a deep learning framework combining state-of-the-
art machine learning algorithms for neural spatiotemporal signal processing. The main goal …

Popular graph neural networks implement convolution operations on graphs based on
polynomial spectral filters. In this paper, we propose a novel graph convolutional layer …

Many recent works in the field of graph machine learning have introduced pooling operators
to reduce the size of graphs. In this article, we present an operational framework to unify this …

Machine learning has huge potential to revolutionize the field of drug discovery and is
attracting increasing attention in recent years. However, lacking domain knowledge (eg …

Short-term prediction of the Electric Vehicle (EV) charging demand is of great importance to
the operation of EV fleets and charging stations. This paper develops a Long Short-Term …

Although there exist several libraries for deep learning on graphs, they are aiming at
implementing basic operations for graph deep learning. In the research community …

Cancer claims millions of lives yearly worldwide. While many therapies have been made
available in recent years, by in large cancer remains unsolved. Exploiting computational …