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.

References

[1]

Long Chen, Fajie Yuan, Joemon M Jose, and Weinan Zhang. 2018. Improving negative sampling for word representation using self-embedded features. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. 99–107.

Digital Library

[2]

Yiqi Chen, Tieyun Qian, Huan Liu, and Ke Sun. 2018. ” Bridge” Enhanced Signed Directed Network Embedding. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 773–782.

Digital Library

[3]

Antonia Creswell, Tom White, Vincent Dumoulin, Kai Arulkumaran, Biswa Sengupta, and Anil A Bharath. 2018. Generative adversarial networks: An overview. IEEE Signal Processing Magazine 35, 1 (2018), 53–65.

[4]

Mihai Cucuringu, Peter Davies, Aldo Glielmo, and Hemant Tyagi. 2019. SPONGE: A generalized eigenproblem for clustering signed networks. In The 22nd International Conference on Artificial Intelligence and Statistics. PMLR, 1088–1098.

[5]

Tyler Derr, Yao Ma, and Jiliang Tang. 2018. Signed graph convolutional networks. In 2018 IEEE International Conference on Data Mining (ICDM). IEEE, 929–934.

[6]

Yoav Goldberg and Omer Levy. 2014. word2vec Explained: deriving Mikolov et al.’s negative-sampling word-embedding method. arXiv preprint arXiv:1402.3722(2014).

[7]

Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. 855–864.

Digital Library

[8]

Junjie Huang, Huawei Shen, Liang Hou, and Xueqi Cheng. 2021. SDGNN: Learning Node Representation for Signed Directed Networks. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 196–203.

[9]

Mohammad Raihanul Islam, B Aditya Prakash, and Naren Ramakrishnan. 2018. Signet: Scalable embeddings for signed networks. In Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 157–169.

Digital Library

[10]

Amin Javari, Tyler Derr, Pouya Esmailian, Jiliang Tang, and Kevin Chen-Chuan Chang. 2020. Rose: Role-based signed network embedding. In Proceedings of The Web Conference 2020. 2782–2788.

Digital Library

[11]

Junghwan Kim, Haekyu Park, Ji-Eun Lee, and U Kang. 2018. Side: representation learning in signed directed networks. In Proceedings of the 2018 World Wide Web Conference. 509–518.

Digital Library

[12]

Srijan Kumar, Bryan Hooi, Disha Makhija, Mohit Kumar, Christos Faloutsos, and VS Subrahmanian. 2018. Rev2: Fraudulent user prediction in rating platforms. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. ACM, 333–341.

Digital Library

[13]

Srijan Kumar, Francesca Spezzano, VS Subrahmanian, and Christos Faloutsos. 2016. Edge weight prediction in weighted signed networks. In Data Mining (ICDM), 2016 IEEE 16th International Conference on. IEEE, 221–230.

[14]

Andrea Lancichinetti and Santo Fortunato. 2009. Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities. Physical Review E 80, 1 (2009), 016118.

[15]

Yeon-Chang Lee, Nayoun Seo, Kyungsik Han, and Sang-Wook Kim. 2020. Asine: Adversarial signed network embedding. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 609–618.

Digital Library

[16]

Jure Leskovec, Daniel Huttenlocher, and Jon Kleinberg. 2010. Predicting positive and negative links in online social networks. In Proceedings of the 19th international conference on World wide web. 641–650.

Digital Library

[17]

Jure Leskovec, Daniel Huttenlocher, and Jon Kleinberg. 2010. Signed networks in social media. In Proceedings of the SIGCHI conference on human factors in computing systems. 1361–1370.

Digital Library

[18]

Omer Levy and Yoav Goldberg. 2014. Neural word embedding as implicit matrix factorization. Advances in neural information processing systems 27 (2014), 2177–2185.

[19]

Yu Li, Yuan Tian, Jiawei Zhang, and Yi Chang. 2020. Learning signed network embedding via graph attention. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 4772–4779.

[20]

Haoxin Liu, Ziwei Zhang, Peng Cui, Yafeng Zhang, Qiang Cui, Jiashuo Liu, and Wenwu Zhu. 2021. Signed Graph Neural Network with Latent Groups. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 1066–1075.

Digital Library

[21]

Si-Yuan Liu, Jing Xiao, and Xiao-Ke Xu. 2020. Sign prediction by motif naive Bayes model in social networks. Information Sciences 541(2020), 316–331.

[22]

Lijia Ma, Yuchun Ma, Qiuzhen Lin, Junkai Ji, Carlos A Coello Coello, and Maoguo Gong. 2020. SNEGAN: Signed Network Embedding by Using Generative Adversarial Nets. IEEE Transactions on Emerging Topics in Computational Intelligence (2020).

[23]

Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems. 3111–3119.

[24]

David Mimno and Laure Thompson. 2017. The strange geometry of skip-gram with negative sampling. In Empirical Methods in Natural Language Processing.

[25]

Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. 701–710.

Digital Library

[26]

Kaare Brandt Petersen, Michael Syskind Pedersen, 2008. The matrix cookbook. Technical University of Denmark 7, 15 (2008), 510.

[27]

Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Chi Wang, Kuansan Wang, and Jie Tang. 2019. Netsmf: Large-scale network embedding as sparse matrix factorization. In The World Wide Web Conference. 1509–1520.

Digital Library

[28]

Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, and Jie Tang. 2018. Network embedding as matrix factorization: Unifying deepwalk, line, pte, and node2vec. In Proceedings of the eleventh ACM international conference on web search and data mining. 459–467.

Digital Library

[29]

Joshua David Robinson, Ching-Yao Chuang, Suvrit Sra, and Stefanie Jegelka. 2020. Contrastive Learning with Hard Negative Samples. In International Conference on Learning Representations.

[30]

Xiao Shen and Fu-Lai Chung. 2018. Deep network embedding for graph representation learning in signed networks. IEEE transactions on cybernetics 50, 4 (2018), 1556–1568.

[31]

Jiliang Tang, Yi Chang, Charu Aggarwal, and Huan Liu. 2016. A survey of signed network mining in social media. ACM Computing Surveys (CSUR) 49, 3 (2016), 1–37.

Digital Library

[32]

Ruo-Chun Tzeng, Bruno Ordozgoiti, and Aristides Gionis. 2020. Discovering conflicting groups in signed networks. In NeurIPS 2020Sun Dec 6th through Sat the 12th.

[33]

Suhang Wang, Jiliang Tang, Charu Aggarwal, Yi Chang, and Huan Liu. 2017. Signed network embedding in social media. In Proceedings of the 2017 SIAM international conference on data mining. SIAM, 327–335.

[34]

Pinghua Xu, Wenbin Hu, Jia Wu, and Bo Du. 2019. Link prediction with signed latent factors in signed social networks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1046–1054.

Digital Library

[35]

Zhen Yang, Ming Ding, Chang Zhou, Hongxia Yang, Jingren Zhou, and Jie Tang. 2020. Understanding negative sampling in graph representation learning. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1666–1676.

Digital Library

[36]

Shuhan Yuan, Xintao Wu, and Yang Xiang. 2017. Sne: signed network embedding. In Pacific-Asia conference on knowledge discovery and data mining. Springer, 183–195.

[37]

Si Zhang, Hanghang Tong, Jiejun Xu, and Ross Maciejewski. 2019. Graph convolutional networks: a comprehensive review. Computational Social Networks 6, 1 (2019), 1–23.

[38]

Yongqi Zhang, Quanming Yao, Yingxia Shao, and Lei Chen. 2019. NSCaching: simple and efficient negative sampling for knowledge graph embedding. In 2019 IEEE 35th International Conference on Data Engineering (ICDE). IEEE, 614–625.

Cited By

Liu ZWang CZheng SWu CZheng KSong YMou N(2025)Pone-GNN: Integrating Positive and Negative Feedback in Graph Neural Networks for Recommender SystemsACM Transactions on Recommender Systems10.1145/37116663:2(1-23)Online publication date: 4-Mar-2025
https://dl.acm.org/doi/10.1145/3711666
Kim MLee YKang DKim S(2024)Trustworthiness-Driven Graph Convolutional Networks for Signed Network EmbeddingACM Transactions on Knowledge Discovery from Data10.1145/368527918:9(1-26)Online publication date: 14-Oct-2024
https://dl.acm.org/doi/10.1145/3685279
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: 1-Apr-2024
https://dl.acm.org/doi/10.1016/j.ins.2024.120373
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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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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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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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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Liu ZWang CZheng SWu CZheng KSong YMou N(2025)Pone-GNN: Integrating Positive and Negative Feedback in Graph Neural Networks for Recommender SystemsACM Transactions on Recommender Systems10.1145/37116663:2(1-23)Online publication date: 4-Mar-2025
https://dl.acm.org/doi/10.1145/3711666
Kim MLee YKang DKim S(2024)Trustworthiness-Driven Graph Convolutional Networks for Signed Network EmbeddingACM Transactions on Knowledge Discovery from Data10.1145/368527918:9(1-26)Online publication date: 14-Oct-2024
https://dl.acm.org/doi/10.1145/3685279
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: 1-Apr-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
Yan DZhang YXie WJin YZhang Y(2023)MUSENeurocomputing10.1016/j.neucom.2022.11.021519:C(36-43)Online publication date: 28-Jan-2023
https://dl.acm.org/doi/10.1016/j.neucom.2022.11.021
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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