research-article

Dual-branch Density Ratio Estimation for Signed Network Embedding

Authors:

Wenbin HuAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 1651 - 1662

https://doi.org/10.1145/3485447.3512171

Published: 25 April 2022 Publication History

Abstract

Signed network embedding (SNE) has received considerable attention in recent years. A mainstream idea of SNE is to learn node representations by estimating the ratio of sampling densities. Though achieving promising performance, these methods based on density ratio estimation are limited to the issues of confusing sample, expected error, and fixed priori. To alleviate the above-mentioned issues, in this paper, we propose a novel dual-branch density ratio estimation (DDRE) architecture for SNE. Specifically, DDRE 1) consists of a dual-branch network, dealing with the confusing sample; 2) proposes the expected matrix factorization without sampling to avoid the expected error; and 3) devises an adaptive cross noise sampling to alleviate the fixed priori. We perform sign prediction and node classification experiments on four real-world and three artificial datasets, respectively. Extensive empirical results demonstrate that DDRE not only significantly outperforms the methods based on density ratio estimation but also achieves competitive performance compared with other types of methods such as graph likelihood, generative adversarial networks, and graph convolutional networks. Code is publicly available at https://github.com/WHU-SNA/DDRE.

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Cited By

Ko GJung J(2024)Learning disentangled representations in signed directed graphs without social assumptionsInformation Sciences: an International Journal10.1016/j.ins.2024.120373665:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.ins.2024.120373
Kim MLee YKim SChen HDuh WHuang HKato MMothe JPoblete B(2023)TrustSGCN: Learning Trustworthiness on Edge Signs for Effective Signed Graph Convolutional NetworksProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3592075(2451-2455)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3592075
Kang DLee WLee YHan KKim S(2022)A Framework for Accurate Community Detection on Signed Networks Using Adversarial LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.323110435:11(10937-10951)Online publication date: 21-Dec-2022
https://dl.acm.org/doi/10.1109/TKDE.2022.3231104

Index Terms

Dual-branch Density Ratio Estimation for Signed Network Embedding
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
    2. Machine learning approaches
      1. Neural networks

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

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Publication History

Published: 25 April 2022

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Research-article
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Funding Sources

Science and Technology Major Project of Hubei Province (Next-Generation AI Technologies)
ARC DECRA
National Natural Science Foundation of China
Joint Fund for Translational Medicine and Interdisciplinary Research of Zhongnan Hospital of Wuhan University

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WWW '22

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SIGWEB

WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

Virtual Event, Lyon, France

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Cited By

Ko GJung J(2024)Learning disentangled representations in signed directed graphs without social assumptionsInformation Sciences: an International Journal10.1016/j.ins.2024.120373665:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.ins.2024.120373
Kim MLee YKim SChen HDuh WHuang HKato MMothe JPoblete B(2023)TrustSGCN: Learning Trustworthiness on Edge Signs for Effective Signed Graph Convolutional NetworksProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3592075(2451-2455)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3592075
Kang DLee WLee YHan KKim S(2022)A Framework for Accurate Community Detection on Signed Networks Using Adversarial LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.323110435:11(10937-10951)Online publication date: 21-Dec-2022
https://dl.acm.org/doi/10.1109/TKDE.2022.3231104

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