Chamberlain, BP;
Clough, JR;
Deisenroth, MP;
(2017)
Neural Embeddings of Graphs in Hyperbolic Space.
In:
Proceedings of 13th International Workshop on Mining and Learning with Graphs.
MLG Workshop 2017
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Abstract
Neural embeddings have been used with great success in Natural Language Processing (NLP). They provide compact representations that encapsulate word similarity and attain state-of-the-art performance in a range of linguistic tasks. The success of neural embeddings has prompted significant amounts of research into applications in domains other than language. One such domain is graph-structured data, where embeddings of vertices can be learned that encapsulate vertex similarity and improve performance on tasks including edge prediction and vertex labelling. For both NLP and graph based tasks, embeddings have been learned in high-dimensional Euclidean spaces. However, recent work has shown that the appropriate isometric space for embedding complex networks is not the flat Euclidean space, but negatively curved, hyperbolic space. We present a new concept that exploits these recent insights and propose learning neural embeddings of graphs in hyperbolic space. We provide experimental evidence that embedding graphs in their natural geometry significantly improves performance on downstream tasks for several real-world public datasets.
| Type: | Proceedings paper |
|---|---|
| Title: | Neural Embeddings of Graphs in Hyperbolic Space |
| Event: | 13th International Workshop on Mining and Learning with Graphs [held in Conjunction with KDD’ 2017], |
| Location: | Halifax (Nova Scotia), Canada |
| Dates: | 14th August 2017 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | http://www.mlgworkshop.org/2017/paper/MLG2017_pape... |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | neural networks, embeddings, graphs, geometry |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10083566 |
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