Search Results for author:
Found 4 papers, 4 papers with code
Graph Convolutional Matrix Completion
We consider matrix completion for recommender systems from the point of view of link prediction on graphs.
Ranked #4 on
Recommendation Systems
on YahooMusic Monti
(using extra training data)
Modeling Relational Data with Graph Convolutional Networks
We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification.
Ranked #1 on
Node Classification
on AIFB
Variational Graph Auto-Encoders
We introduce the variational graph auto-encoder (VGAE), a framework for unsupervised learning on graph-structured data based on the variational auto-encoder (VAE).
Ranked #1 on
Link Prediction
on Pubmed
(ACC metric)
Semi-Supervised Classification with Graph Convolutional Networks
We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs.
Ranked #1 on
Graph Property Prediction
on ogbg-ppa
