Zoo guide to network embedding
Zoo guide to network embedding
Networks have provided extremely successful models of data and complex systems. Yet, as combinatorial objects, networks do not have in general intrinsic coordinates and do not typically lie in an ambient space. The process of assigning an embedding space to a network has attracted great interest in the past few decades, and has been efficiently applied to fundamental problems in network inference, such as link prediction, node classification, and community detection. In this review, we provide a user-friendly guide to the network embedding literature and current trends in this field which will allow the reader to navigate through the complex landscape of methods and approaches emerging from the vibrant research activity on these subjects.
higher-order network, network analysis, network embedding, representation learning
Baptista, A.
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Sánchez-Garcia, R.J.
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Baudot, A.
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Bianconi, G.
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1 December 2023
Baptista, A.
3ca93c8d-b361-4b16-ab36-2b08df637251
Sánchez-Garcia, R.J.
8246cea2-ae1c-44f2-94e9-bacc9371c3ed
Baudot, A.
b606106f-4f25-4d44-ac13-1acff371f451
Bianconi, G.
654d0a71-cae6-4e5a-98c3-b40b3d8a2a96
Baptista, A., Sánchez-Garcia, R.J., Baudot, A. and Bianconi, G.
(2023)
Zoo guide to network embedding.
Journal of Physics: Complexity, 4 (4), [042001].
(doi:10.1088/2632-072X/ad0e23).
Abstract
Networks have provided extremely successful models of data and complex systems. Yet, as combinatorial objects, networks do not have in general intrinsic coordinates and do not typically lie in an ambient space. The process of assigning an embedding space to a network has attracted great interest in the past few decades, and has been efficiently applied to fundamental problems in network inference, such as link prediction, node classification, and community detection. In this review, we provide a user-friendly guide to the network embedding literature and current trends in this field which will allow the reader to navigate through the complex landscape of methods and approaches emerging from the vibrant research activity on these subjects.
Text
Baptista_2023_J._Phys._Complex._4_042001
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Accepted/In Press date: 20 November 2023
e-pub ahead of print date: 29 November 2023
Published date: 1 December 2023
Additional Information:
Funding Information:
We acknowledge interesting discussions with Filippo Radicchi and funding from the Roche-Turing Partnership (A Baptista, R S-G, G B) and the ‘Investissements d’Avenir’ French Government program managed by the French National Research Agency (ANR-16-CONV-0001 and ANR-21-CE45-0001-01) (A Baudot). We also acknowledge Galadriel Brière for interesting discussions.
Publisher Copyright:
© 2023 The Author(s). Published by IOP Publishing Ltd.
Keywords:
higher-order network, network analysis, network embedding, representation learning
Identifiers
Local EPrints ID: 485453
URI: http://eprints.soton.ac.uk/id/eprint/485453
ISSN: 2632-072X
PURE UUID: a0267dde-6841-4d1e-9249-4acbd01da9d9
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Date deposited: 06 Dec 2023 17:49
Last modified: 18 Mar 2024 03:16
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Author:
A. Baptista
Author:
A. Baudot
Author:
G. Bianconi
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