T
Thomas Kipf
Researcher at University of Amsterdam
Publications - 34
Citations - 29059
Thomas Kipf is an academic researcher from University of Amsterdam. The author has contributed to research in topics: Graph (abstract data type) & Feature learning. The author has an hindex of 21, co-authored 34 publications receiving 17582 citations. Previous affiliations of Thomas Kipf include University of Erlangen-Nuremberg & Google.
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Semi-Supervised Classification with Graph Convolutional Networks
Thomas Kipf,Max Welling +1 more
TL;DR: 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 which outperforms related methods by a significant margin.
Proceedings Article
Semi-Supervised Classification with Graph Convolutional Networks
Thomas Kipf,Max Welling +1 more
TL;DR: In this paper, a scalable approach for semi-supervised learning on graph-structured data is presented based on an efficient variant of convolutional neural networks which operate directly on graphs.
Book ChapterDOI
Modeling Relational Data with Graph Convolutional Networks
Michael Sejr Schlichtkrull,Thomas Kipf,Peter Bloem,Rianne van den Berg,Ivan Titov,Ivan Titov,Max Welling,Max Welling +7 more
TL;DR: It is shown that factorization models for link prediction such as DistMult can be significantly improved through the use of an R-GCN encoder model to accumulate evidence over multiple inference steps in the graph, demonstrating a large improvement of 29.8% on FB15k-237 over a decoder-only baseline.
Posted Content
Variational Graph Auto-Encoders
Thomas Kipf,Max Welling +1 more
TL;DR: The variational graph auto-encoder (VGAE) is introduced, a framework for unsupervised learning on graph-structured data based on the variational auto- Encoder (VAE) that can naturally incorporate node features, which significantly improves predictive performance on a number of benchmark datasets.
Posted Content
Modeling Relational Data with Graph Convolutional Networks
Michael Sejr Schlichtkrull,Thomas Kipf,Peter Bloem,Rianne van den Berg,Ivan Titov,Ivan Titov,Max Welling,Max Welling +7 more
TL;DR: Relational Graph Convolutional Networks (R-GCNets) as discussed by the authors are related to a recent class of neural networks operating on graphs, and are developed specifically to deal with the highly multi-relational data characteristic of realistic knowledge bases.