21 citations · 35 across the 3 of their papers we have counts for
5 papers
GRAND: Graph Neural Diffusion
Benjamin Paul Chamberlain, James Rowbottom, Maria Gorinova +3
We present Graph Neural Diffusion (GRAND) that approaches deep learning on graphs as a continuous diffusion process and treats Graph Neural Networks (GNNs) as discretisations of an…
Tuning Word2vec for Large Scale Recommendation Systems
Benjamin P. Chamberlain, Emanuele Rossi, Dan Shiebler +2
Word2vec is a powerful machine learning tool that emerged from Natural Lan-guage Processing (NLP) and is now applied in multiple domains, including recom-mender systems, forecastin…
Temporal Graph Networks for Deep Learning on Dynamic Graphs
Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca +3
Graph Neural Networks (GNNs) have recently become increasingly popular due to their ability to learn complex systems of relations or interactions arising in a broad spectrum of pro…
SIGN: Scalable Inception Graph Neural Networks
Fabrizio Frasca, Emanuele Rossi, Davide Eynard +3
Graph representation learning has recently been applied to a broad spectrum of problems ranging from computer graphics and chemistry to high energy physics and social media. The po…
ncRNA Classification with Graph Convolutional Networks
Emanuele Rossi, Federico Monti, Michael Bronstein +1
Non-coding RNA (ncRNA) are RNA sequences which don't code for a gene but instead carry important biological functions. The task of ncRNA classification consists in classifying a gi…