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Deep Partial Multiplex Network Embedding

Authors:

Anirudh Ravula,

Dongfang LiuAuthors Info & Claims

WWW '22: Companion Proceedings of the Web Conference 2022

Pages 1053 - 1062

https://doi.org/10.1145/3487553.3524717

Published: 16 August 2022 Publication History

Abstract

Network embedding is an effective technique to learn the low-dimensional representations of nodes in networks. Real-world networks are usually with multiplex or having multi-view representations from different relations. Recently, there has been increasing interest in network embedding on multiplex data. However, most existing multiplex approaches assume that the data is complete in all views. But in real applications, it is often the case that each view suffers from the missing of some data and therefore results in partial multiplex data.

In this paper, we present a novel Deep Partial Multiplex Network Embedding approach to deal with incomplete data. In particular, the network embeddings are learned by simultaneously minimizing the deep reconstruction loss with the autoencoder neural network, enforcing the data consistency across views via common latent subspace learning, and preserving the data topological structure within the same network through graph Laplacian. We further prove the orthogonal invariant property of the learned embeddings and connect our approach with the binary embedding techniques. Experiments on four multiplex benchmarks demonstrate the superior performance of the proposed approach over several state-of-the-art methods on node classification, link prediction and clustering tasks.

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Cited By

Wang CTang Z(2024)The Staged Knowledge Distillation in Video Classification: Harmonizing Student Progress by a Complementary Weakly Supervised FrameworkIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.329497734:8(6646-6660)Online publication date: Aug-2024
https://doi.org/10.1109/TCSVT.2023.3294977
Tang JYi QFu STian Y(2024)Incomplete multi-view learningApplied Soft Computing10.1016/j.asoc.2024.111278153:COnline publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111278
Chen KQiu YLiu ZMu W(2024)Layer imbalance-aware multiplex network embeddingKnowledge and Information Systems10.1007/s10115-024-02072-z66:6(3547-3569)Online publication date: 5-Mar-2024
https://dl.acm.org/doi/10.1007/s10115-024-02072-z

Index Terms

Deep Partial Multiplex Network Embedding
1. Information systems
  1. World Wide Web
    1. Web searching and information discovery

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cover image ACM Conferences

WWW '22: Companion Proceedings of the Web Conference 2022

April 2022

1338 pages

ISBN:9781450391306

DOI:10.1145/3487553

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Lionel Médini
Université Lyon 1, France
,
Ivan Herman
W3C / retired

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Published: 16 August 2022

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April 25 - 29, 2022

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Cited By

Wang CTang Z(2024)The Staged Knowledge Distillation in Video Classification: Harmonizing Student Progress by a Complementary Weakly Supervised FrameworkIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.329497734:8(6646-6660)Online publication date: Aug-2024
https://doi.org/10.1109/TCSVT.2023.3294977
Tang JYi QFu STian Y(2024)Incomplete multi-view learningApplied Soft Computing10.1016/j.asoc.2024.111278153:COnline publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111278
Chen KQiu YLiu ZMu W(2024)Layer imbalance-aware multiplex network embeddingKnowledge and Information Systems10.1007/s10115-024-02072-z66:6(3547-3569)Online publication date: 5-Mar-2024
https://dl.acm.org/doi/10.1007/s10115-024-02072-z
Zhang JWang FZhang HShi X(2023)Compressive sensing spatially adaptive total variation method for high-noise astronomical image denoisingThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-023-02842-w40:2(1215-1227)Online publication date: 28-Mar-2023
https://dl.acm.org/doi/10.1007/s00371-023-02842-w

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