HEAT: Hyperbolic Embedding of Attributed Networks

David McDonald*, Shan He

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Downloads (Pure)


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.

Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning – IDEAL 2020
Subtitle of host publication21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part I
EditorsCesar Analide, Paulo Novais, David Camacho, Hujun Yin
Number of pages13
ISBN (Electronic)9783030623623
ISBN (Print)9783030623616
Publication statusPublished - 30 Oct 2020
Event21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020 - Guimaraes, Portugal
Duration: 4 Nov 20206 Nov 2020

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Copyright 2020 Elsevier B.V., All rights reserved.


  • Network embedding
  • Hyperbolic embedding
  • Random walk

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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