research-article

SGCL: Contrastive Representation Learning for Signed Graphs

Authors:

Shaofeng ShenAuthors Info & Claims

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

Pages 1671 - 1680

https://doi.org/10.1145/3459637.3482478

Published: 30 October 2021 Publication History

Abstract

Graph contrastive representation learning aims to learn discriminative node representations by contrasting positive and negative samples. It helps models learn more generalized representations to achieve better performances on downstream tasks, which has aroused increasing research interest in recent years. Simultaneously, signed graphs consisting of both positive and negative links have become ubiquitous with the growing popularity of social media. However, existing works on graph contrastive representation learning are only proposed for unsigned graphs (containing only positive links) and it remains unexplored how they could be applied to signed graphs due to the distinct semantics and complex relations between positive and negative links. Therefore we propose a novel Signed Graph Contrastive Learning model (SGCL) to bridge this gap, which to the best of our knowledge is the first research to employ graph contrastive representation learning on signed graphs. Concretely, we design two types of graph augmentations specific to signed graphs based on a significant signed social theory, i.e., balance theory. Besides, inter-view and intra-view contrastive learning are proposed to learn discriminative node representations from perspectives of graph augmentations and signed structures respectively. Experimental results demonstrate the superiority of the proposed model over state-of-the-art methods on both real-world social datasets and online game datasets.

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References

[1]

Dorwin Cartwright and Frank Harary. 1956. Structural balance: a generalization of Heider's theory. Psychological review, Vol. 63, 5 (1956), 277.

[2]

Liang Chen, Yuanzhen Xie, Zibin Zheng, Huayou Zheng, and Jingdun Xie. 2020. Friend Recommendation Based on Multi-Social Graph Convolutional Network. IEEE Access, Vol. 8 (2020), 43618--43629.

[3]

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

Digital Library

[4]

Yiqi Chen, Tieyun Qian, Ming Zhong, and Xuhui Li. 2018b. BASSI: Balance and Status Combined Signed Network Embedding. In International Conference on Database Systems for Advanced Applications. Springer, 55--63.

[5]

Tyler Derr. 2020. Network analysis with negative links. In Proceedings of the 13th International Conference on Web Search and Data Mining. 917--918.

Digital Library

[6]

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

[7]

Michael Gutmann and Aapo Hyv"arinen. 2010. Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. JMLR Workshop and Conference Proceedings, 297--304.

[8]

Kaveh Hassani and Amir Hosein Khasahmadi. 2020. Contrastive multi-view representation learning on graphs. In International Conference on Machine Learning. PMLR, 4116--4126.

[9]

Fritz Heider. 1946. Attitudes and cognitive organization. The Journal of psychology, Vol. 21, 1 (1946), 107--112.

[10]

Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec. 2019. Strategies for pre-training graph neural networks. arXiv preprint arXiv:1905.12265 (2019).

[11]

Junjie Huang, Huawei Shen, Liang Hou, and Xueqi Cheng. 2019. Signed graph attention networks. In International Conference on Artificial Neural Networks. Springer, 566--577.

Digital Library

[12]

Junjie Huang, Huawei Shen, Liang Hou, and Xueqi Cheng. 2021. SDGNN: Learning Node Representation for Signed Directed Networks. arXiv preprint arXiv:2101.02390 (2021).

[13]

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

[14]

Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Debapriya Banerjee, and Fillia Makedon. 2021. A survey on contrastive self-supervised learning. Technologies, Vol. 9, 1 (2021), 2.

[15]

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

[16]

Wei Jin, Tyler Derr, Haochen Liu, Yiqi Wang, Suhang Wang, Zitao Liu, and Jiliang Tang. 2020. Self-supervised learning on graphs: Deep insights and new direction. arXiv preprint arXiv:2006.10141 (2020).

[17]

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

[18]

Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).

[19]

Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016).

[20]

Srijan Kumar, William L Hamilton, Jure Leskovec, and Dan Jurafsky. 2018. Community interaction and conflict on the web. In Proceedings of the 2018 world wide web conference. 933--943.

Digital Library

[21]

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

[22]

Phuc H Le-Khac, Graham Healy, and Alan F Smeaton. 2020. Contrastive representation learning: A framework and review. IEEE Access (2020).

[23]

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

[24]

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.

[25]

Xiao Liu, Fanjin Zhang, Zhenyu Hou, Zhaoyu Wang, Li Mian, Jing Zhang, and Jie Tang. 2020b. Self-supervised learning: Generative or contrastive. arXiv preprint arXiv:2006.08218, Vol. 1, 2 (2020).

[26]

Yang Liu, Chen Liang, Xiangnan He, Jiaying Peng, Zibin Zheng, and Jie Tang. 2020a. Modelling High-Order Social Relations for Item Recommendation. IEEE Transactions on Knowledge and Data Engineering (2020).

[27]

Silviu Maniu, Bogdan Cautis, and Talel Abdessalem. 2011. Building a signed network from interactions in Wikipedia. In Databases and Social Networks. 19--24.

Digital Library

[28]

Alexandru Mara, Yoosof Mashayekhi, Jefrey Lijffijt, and Tijl De Bie. 2020. CSNE: Conditional Signed Network Embedding. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 1105--1114.

Digital Library

[29]

Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018).

[30]

Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. arXiv preprint arXiv:1912.01703 (2019).

[31]

Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, and Jie Tang. 2020. Gcc: Graph contrastive coding for graph neural network pre-training. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1150--1160.

Digital Library

[32]

Kihyuk Sohn. 2016. Improved deep metric learning with multi-class n-pair loss objective. In Proceedings of the 30th International Conference on Neural Information Processing Systems. 1857--1865.

Digital Library

[33]

Dongjin Song and David A Meyer. 2015. Link sign prediction and ranking in signed directed social networks. Social network analysis and mining, Vol. 5, 1 (2015), 1--14.

[34]

Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, and Jian Tang. 2019. Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. arXiv preprint arXiv:1908.01000 (2019).

[35]

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

Digital Library

[36]

Jiliang Tang, Xia Hu, and Huan Liu. 2014. Is distrust the negation of trust? The value of distrust in social media. In Proceedings of the 25th ACM conference on Hypertext and social media. 148--157.

Digital Library

[37]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. arXiv preprint arXiv:1706.03762 (2017).

[38]

Petar Velivc ković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017).

[39]

Petar Velivc ković, William Fedus, William L Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm. 2018. Deep graph infomax. arXiv preprint arXiv:1809.10341 (2018).

[40]

Hongwei Wang, Fuzheng Zhang, Min Hou, Xing Xie, Minyi Guo, and Qi Liu. 2018. Shine: Signed heterogeneous information network embedding for sentiment link prediction. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. 592--600.

Digital Library

[41]

Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, et al. 2019. Deep graph library: A graph-centric, highly-performant package for graph neural networks. arXiv preprint arXiv:1909.01315 (2019).

[42]

Suhang Wang, Charu Aggarwal, Jiliang Tang, and Huan Liu. 2017a. Attributed signed network embedding. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 137--146.

Digital Library

[43]

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

[44]

Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. 2020. A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems (2020).

[45]

Fenfang Xie, Angyu Zheng, Liang Chen, and Zibin Zheng. 2021b. Attentive Meta-graph Embedding for item Recommendation in heterogeneous information networks. Knowledge-Based Systems, Vol. 211 (2021), 106524.

[46]

Yaochen Xie, Zhao Xu, Jingtun Zhang, Zhengyang Wang, and Shuiwang Ji. 2021a. Self-supervised learning of graph neural networks: A unified review. arXiv preprint arXiv:2102.10757 (2021).

[47]

Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020. Graph contrastive learning with augmentations. Advances in Neural Information Processing Systems, Vol. 33 (2020).

[48]

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.

[49]

Tong Zhao, Yozen Liu, Leonardo Neves, Oliver Woodford, Meng Jiang, and Neil Shah. 2020. Data Augmentation for Graph Neural Networks. arXiv preprint arXiv:2006.06830 (2020).

[50]

Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun. 2020. Graph neural networks: A review of methods and applications. AI Open, Vol. 1 (2020), 57--81.

[51]

Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. 2020a. Deep graph contrastive representation learning. arXiv preprint arXiv:2006.04131 (2020).

[52]

Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. 2020b. Graph Contrastive Learning with Adaptive Augmentation. arXiv preprint arXiv:2010.14945 (2020).

Digital Library

Cited By

Pan YJi XYou JLi LLiu ZZhang XZhang ZWang M(2025)CSGDN: contrastive signed graph diffusion network for predicting crop gene–phenotype associationsBriefings in Bioinformatics10.1093/bib/bbaf06226:1Online publication date: 20-Feb-2025
https://doi.org/10.1093/bib/bbaf062
He CCheng HYang JTang YGuan Q(2025)Signed graph embedding via multi-order neighborhood feature fusion and contrastive learningNeural Networks10.1016/j.neunet.2024.106897182:COnline publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1016/j.neunet.2024.106897
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
Show More Cited By

Index Terms

SGCL: Contrastive Representation Learning for Signed Graphs
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Learning latent representations
2. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory
      1. Graph algorithms

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cover image ACM Conferences

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

October 2021

4966 pages

ISBN:9781450384469

DOI:10.1145/3459637

General Chairs:
Gianluca Demartini
The University of Queensland, Australia
,
Guido Zuccon
The University of Queensland, Australia
,
Program Chairs:
J. Shane Culpepper
RMIT University, Australia
,
Zi Huang
The University of Queensland, Australia
,
Hanghang Tong
University of Illinois at Urbana-Champaign, USA

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Published: 30 October 2021

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the Guangdong Basic and Applied Basic Research Foundation
UX Center, Netease Games
the Natural Science Foundation of Guangdong
the National Natural Science Foundation of China
the Key-Area Research and Development Program of Guangdong Provinc

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CIKM '21

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CIKM '21: The 30th ACM International Conference on Information and Knowledge Management

November 1 - 5, 2021

Queensland, Virtual Event, Australia

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Pan YJi XYou JLi LLiu ZZhang XZhang ZWang M(2025)CSGDN: contrastive signed graph diffusion network for predicting crop gene–phenotype associationsBriefings in Bioinformatics10.1093/bib/bbaf06226:1Online publication date: 20-Feb-2025
https://doi.org/10.1093/bib/bbaf062
He CCheng HYang JTang YGuan Q(2025)Signed graph embedding via multi-order neighborhood feature fusion and contrastive learningNeural Networks10.1016/j.neunet.2024.106897182:COnline publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1016/j.neunet.2024.106897
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
Minici MCinus FBonchi FManco GSerra ESpezzano F(2024)Link Polarity Prediction from Sparse and Noisy Labels via Multiscale Social BalanceProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679786(1689-1699)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679786
Fang ZTan SWang Y(2024)A Signed Subgraph Encoding Approach via Linear Optimization for Link Sign PredictionIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.328092435:10(14659-14670)Online publication date: Oct-2024
https://doi.org/10.1109/TNNLS.2023.3280924
Zhu YMichalak TLuo XZhang XZhou K(2024)Toward Secrecy-Aware Attacks Against Trust Prediction in Signed Social NetworksIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.336436619(3567-3580)Online publication date: 2024
https://doi.org/10.1109/TIFS.2024.3364366
Qi YDu EShu LChen C(2024)SGCA: Signed Graph Contrastive Learning with Adaptive Augmentation2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651025(1-10)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10651025
Zhou JLai YRen JZhou K(2024)Black-Box Attacks Against Signed Graph Analysis via Balance Poisoning2024 International Conference on Computing, Networking and Communications (ICNC)10.1109/ICNC59896.2024.10556322(530-535)Online publication date: 19-Feb-2024
https://doi.org/10.1109/ICNC59896.2024.10556322
Wan S(2024)Learning from Subgraphs and Embeddings for Sign Prediction2024 21st International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)10.1109/ICCWAMTIP64812.2024.10873642(01-05)Online publication date: 14-Dec-2024
https://doi.org/10.1109/ICCWAMTIP64812.2024.10873642
Wang SNi LZhang ZLi XZheng XLiu J(2024)Multimodal prediction of student performance: A fusion of signed graph neural networks and large language modelsPattern Recognition Letters10.1016/j.patrec.2024.03.007181(1-8)Online publication date: May-2024
https://doi.org/10.1016/j.patrec.2024.03.007
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