Due to their great performance in many challenges, Deep Learning (DL) techniques keep gaining popularity in many fields. They have been adapted to process graph data structures to solve various ...

Inspired by the powerful data modelling and prediction capabilities of deep learning techniques, we explore the possibility of applying deep learning techniques to graph drawing. Specifically, we ...

Deep Learning and Machine Learning has made breakthroughs in recent years. There is tens of billions of dollars going into development of the new AI. Google and Deep Mind are recognizing that Deep ...

Deep Learning for Graphs Has a Long-Standing History. The deep learning for graphs field is rooted in neural networks for graphs research and early 1990s works on Recursive Neural Networks (RecNN) for ...

Deep learning, meet knowledge graphs . When asked if he thinks knowledge graphs can have a role in the hybrid approach he advocates for, Marcus was positive. One way to think about it, ...

This repository contains a curated list of papers on deep graph learning for drug discovery (DGL4DD), which are categorized based on their published years and corresponding tasks. Matching receptor to ...