Finding a low dimensional representation of hierarchical, structured data described by a network remains a challenging problem in the machine learning community. An emerging approach is embedding networks into hyperbolic space since it can naturally represent a network’s hierarchical structure. However, existing hyperbolic embedding approaches cannot deal with attributed networks, where nodes are annotated with additional attributes. To overcome this, we propose HEAT (for Hyperbolic Embedding of Attributed Networks). HEAT first extracts training samples from the network that captures both topological and attribute node similarity and then learns a low-dimensional hyperboloid embedding using full Riemannian Stochastic Gradient Descent. We show that HEAT can outperform other network embedding algorithms on several common downstream tasks. As a general network embedding method, HEAT opens the door to hyperbolic manifold learning on a wide range of both attributed and unattributed networks.
|Title of host publication||Intelligent Data Engineering and Automated Learning – IDEAL 2020 - 21st International Conference, 2020, Proceedings|
|Editors||Cesar Analide, Paulo Novais, David Camacho, Hujun Yin|
|Number of pages||13|
|Publication status||Published - 2020|
|Event||21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020 - Guimaraes, Portugal|
Duration: 4 Nov 2020 → 6 Nov 2020
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020|
|Period||4/11/20 → 6/11/20|
Bibliographical notePublisher Copyright:
© 2020, Springer Nature Switzerland AG.
Copyright 2020 Elsevier B.V., All rights reserved.
- Hyperbolic embedding
- Network embedding
- Random walk
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)