HEAT: Hyperbolic Embedding of Attributed Networks

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

Standard

HEAT : Hyperbolic Embedding of Attributed Networks. / McDonald, David; He, Shan.

Intelligent Data Engineering and Automated Learning – IDEAL 2020 - 21st International Conference, 2020, Proceedings. ed. / Cesar Analide; Paulo Novais; David Camacho; Hujun Yin. Springer, 2020. p. 28-40 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12489 LNCS).

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

Harvard

McDonald, D & He, S 2020, HEAT: Hyperbolic Embedding of Attributed Networks. in C Analide, P Novais, D Camacho & H Yin (eds), Intelligent Data Engineering and Automated Learning – IDEAL 2020 - 21st International Conference, 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12489 LNCS, Springer, pp. 28-40, 21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020, Guimaraes, Portugal, 4/11/20. https://doi.org/10.1007/978-3-030-62362-3_4

APA

McDonald, D., & He, S. (2020). HEAT: Hyperbolic Embedding of Attributed Networks. In C. Analide, P. Novais, D. Camacho, & H. Yin (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2020 - 21st International Conference, 2020, Proceedings (pp. 28-40). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12489 LNCS). Springer. https://doi.org/10.1007/978-3-030-62362-3_4

Vancouver

McDonald D, He S. HEAT: Hyperbolic Embedding of Attributed Networks. In Analide C, Novais P, Camacho D, Yin H, editors, Intelligent Data Engineering and Automated Learning – IDEAL 2020 - 21st International Conference, 2020, Proceedings. Springer. 2020. p. 28-40. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-62362-3_4

Author

McDonald, David ; He, Shan. / HEAT : Hyperbolic Embedding of Attributed Networks. Intelligent Data Engineering and Automated Learning – IDEAL 2020 - 21st International Conference, 2020, Proceedings. editor / Cesar Analide ; Paulo Novais ; David Camacho ; Hujun Yin. Springer, 2020. pp. 28-40 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Bibtex

@inproceedings{460c050a9e784e4691846ef8092c409a,
title = "HEAT: Hyperbolic Embedding of Attributed Networks",
abstract = "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{\textquoteright}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.",
keywords = "Hyperbolic embedding, Network embedding, Random walk",
author = "David McDonald and Shan He",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020 ; Conference date: 04-11-2020 Through 06-11-2020",
year = "2020",
doi = "10.1007/978-3-030-62362-3_4",
language = "English",
isbn = "9783030623616",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "28--40",
editor = "Cesar Analide and Paulo Novais and David Camacho and Hujun Yin",
booktitle = "Intelligent Data Engineering and Automated Learning – IDEAL 2020 - 21st International Conference, 2020, Proceedings",

}

RIS

TY - GEN

T1 - HEAT

T2 - 21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020

AU - McDonald, David

AU - He, Shan

N1 - Publisher Copyright: © 2020, Springer Nature Switzerland AG. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Hyperbolic embedding

KW - Network embedding

KW - Random walk

UR - http://www.scopus.com/inward/record.url?scp=85097442941&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-62362-3_4

DO - 10.1007/978-3-030-62362-3_4

M3 - Conference contribution

AN - SCOPUS:85097442941

SN - 9783030623616

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 28

EP - 40

BT - Intelligent Data Engineering and Automated Learning – IDEAL 2020 - 21st International Conference, 2020, Proceedings

A2 - Analide, Cesar

A2 - Novais, Paulo

A2 - Camacho, David

A2 - Yin, Hujun

PB - Springer

Y2 - 4 November 2020 through 6 November 2020

ER -