Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

Graph machine learning (GML) is receiving growing interest within the pharmaceutical and
biotechnology industries for its ability to model biomolecular structures, the functional …

Travel-time prediction constitutes a task of high importance in transportation networks, with
web map** services like Google Maps regularly serving vast quantities of travel time …

Graph is a natural data structure for describing complex systems, which contains a set of
objects and relationships. Ubiquitous real-life biomedical problems can be modeled as …

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 …

Transformer architectures have widespread applications, particularly in Natural Language
Processing and Computer Vision. Recently, Transformers have been employed in various …

Deep learning systems have been successfully applied to Euclidean data such as images,
video, and audio. In many applications, however, information and their relationships are …

A graph neural network (GNN) enables deep learning on structured graph data. There are
two major GNN training obstacles: 1) it relies on high-end servers with many GPUs which …

Emerging as fundamental building blocks for diverse artificial intelligence applications,
foundation models have achieved notable success across natural language processing and …

Graph neural networks (GNNs) have been demonstrated to be an effective model for
learning tasks related to graph structured data. Different from classical deep neural networks …