Skip to main content
SpringerLink
Log in

A Novel Functional Network Based on Three-way Decision for Link Prediction in Signed Social Networks

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

Aiming to reveal the potential relationships between users, link prediction has been considered as a fundamental research issue in signed social networks. The key of the link prediction is to measure the similarity between users. Many existing researches use connections between users and their common neighbors to measure the similarities, and these methods rely too much on the structure of social networks. Most of them use the deep neural network to enhance the prediction accuracy. However, the complete structure of the huge social network cannot be captured easily, and the models learnt by the deep neural network are unexplainable and uncontrolled. As an explainable model, functional network is a recent replacement for standard neural network. Therefore, we revise the traditional strategy of functional network and propose a novel functional network framework. Firstly, the attributes are preprocessed through the cloud model to define their importance before inputting them into the functional network. Then the association algorithm is used to do aggregate computation in computing neurons for defining the connections between neurons well. Finally, we use three-way decisions to process the samples in the boundary to optimize the performance of model. Experiments executed on six real datasets show that our method has significantly higher link prediction precision than the state-of-the-art works. From our discussions, the improved functional network can be a valid replacement for neural networks in some fields.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Wang S, Tang J, Aggarwal C, Chang Y, Liu H. Signed network embedding in social media. In Society for Indus Appl Math. 2017; pp. 327-335.

  2. Liu S, Jiang C, Lin Z, Ding Y, Duan R, Xu Z. Identifying effective influencers based on trust for electronic word-of-mouth marketing: A domain-aware approach. Info Sci. 2015;306:34–52.

    Article  Google Scholar 

  3. Liu H, Hu Z, Mian A, Tian H, Zhu X. A new user similarity model to improve the accuracy of collaborative filtering. Knowl-Based Syst. 2014;56:156–66.

    Article  Google Scholar 

  4. Libe-Nowell D, Kleinberg J. The link-prediction problem for social networks. J Am Soc Info Sci Tech. 2007;58(7):1019–31.

    Article  Google Scholar 

  5. Wang D, Liang Y, Xu D. A content-based recommender system for computer science publications. Knowl-Based Syst. 2018;157:1–9.

    Article  Google Scholar 

  6. Fan RE, Chang KW, Hsieh CJ, Wang XR, Lin CJ. LIBLINEAR: A library for large linear classification. J Mach Learn Res. 2008;1871-1874.

  7. Feng Y, Chen H. An Improved Density Peaks Clustering based on Rough Set Theory for Overlapping Community Detection. Int Conf Intell Syst Knowl Eng. 2019;21-28.

  8. Zhu X, Ghahramani Z. Learning from labels and unlabeled data with label propagation. Tech Report. 2004;2002(3175):237–44.

    Google Scholar 

  9. Zhou D, Bousquet O, Lal TN, Weston J, Schkopf B. Learning with local and global consistency. In Advances in neural information processing systems. 2004; pp. 321-328.

  10. Cen Y, Zhang J, Wang G, Qian Y, Meng C, Dai Z, Tang J. Trust Relationship Prediction in Alibaba E-Commerce Platform. IEEE Trans Knowl Data Eng. 2019;32(5):1024–35.

    Article  Google Scholar 

  11. Qun Liu, Shuxin Liu, Guoyin Wang. Social Relationship Prediction across Networks Using Tri-training BP Neral Networks. Neurocomputing. 2020;401:377–91.

    Article  Google Scholar 

  12. Kipf TN, Welling M. Semi-supervised classification with graph convolutional network. https://arxiv.org/abs/1609.02907.

  13. Kipf TN, Welling M. Variational graph auto-encoders. https://arxiv.org/abs/1611.07308.

  14. Huang J, Shen H, Hou L, Cheng X. Signed graph attention network. In International Conference on Artificial Neural Network. 2019; pp. 566-577.

  15. Veli P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y. Graph attention network. https://arxiv.org/abs/1710.10903.

  16. Yao Y. An outline of a theory of three-way decisions. In International Conference on Rough Sets and Current Trends in Computing. 2012; pp. 1-17.

  17. Yao Y. Three-way decision: an interpretation of rules in rough set theory. In International Conference on Rough Sets and Knowledge Technology. 2009; pp. 642-649.

  18. Yao Y. Decision-theoretic rough set models, in: International Conference on Rough Sets and Knowledge Technology, Springer. 2007; pp. 1-12.

  19. Yao Y. Three-way granular computing, rough sets, and formal concept analysis. Int J Approx Reas. 2020;2020(116):106–25.

    Article  MathSciNet  MATH  Google Scholar 

  20. Yao Y. Three-way decision and granular computing. Int J Approx Reas. 2018;2018(103):107–23.

    Article  MATH  Google Scholar 

  21. Yu H, Chen Y, Lingras P, Wang GY. A three-way cluster ensemble approach for large-scale data. Int J Approx Reas. 2019;2019(115):32–49.

    Article  MathSciNet  MATH  Google Scholar 

  22. Afridi MK, Azam N, Yao JT, Alanazi E. A three-way clustering approach for handling missing data using GTRS. Int J Approx Reas. 2018;2018(98):11–24.

    Article  MathSciNet  MATH  Google Scholar 

  23. Yao JT, Azam N. Web-based medical decision support systems for three-way medical decision making with game-theoretic rough sets. IEEE Trans Fuzzy Syst. 2015;23(1):3-15.

  24. Zhang HR, Min F, Shi B. Regression-based three-way recommendation. Info Sci. 2017;378(2017):444-461.

  25. Li D. Membership clouds and membership cloud generators. Comp Res Dev. 1995;32(6):15–20.

    Google Scholar 

  26. Du Y, Li DY. Concept partition based on cloud and its application to mining association rules. J Soft. 2001;12(2):196–203.

    Google Scholar 

  27. Jiang R, Li DY. Similarity search based on shape representation in time-series data sets. J Comp Res Dev. 2000;37(5):601–8.

    MathSciNet  Google Scholar 

  28. Song Y, Li D. Reliability evaluation of electronic products based on cloud models. Acta Electronica Sinica. 2000;28(12):74–6.

    Google Scholar 

  29. Castillo E, Gutierrez JM. Nonlinear time series modeling and prediction using functional network. Extracting information masked by chaos. Phys Lett A. 1998; 244;(1-3):71-84.

  30. Castillo E, Hadi AS. Functional network. Wiley StatsRef: Statistics Reference Online; 2014.

    Book  Google Scholar 

  31. Li D, Han J, Shi X, Chan MC. Knowledge representation and discovery based on linguistic atoms. Knowl-Based Syst. 1998;10(7):431–40.

    Article  Google Scholar 

  32. Wang G, Xu C, Li D. Generic normal cloud model. Info Sci. 2014;280:1–15.

    Article  MathSciNet  MATH  Google Scholar 

  33. Loia V, Parente D, Pedrycz W, Tomasiello S. A granular functional network with delay: some dynamical properties and application to the sign prediction in social network. Neurocomputing. 2018;321:61–71.

    Article  Google Scholar 

  34. Yang J, Wang G, Liu Q, Guo Y, Liu Y, Gan W, Liu Y. Retrospect and Prospect of Research of Normal Cloud Model. Chinese J Comp. 2018;41(03):724–44.

    Google Scholar 

  35. Zhou G, Zhou Y, Huang H, Tang Z. Functional networks and applications: A survey. Neurocomputing. 2019;335:384–99.

    Article  Google Scholar 

  36. Tang J, Gao H, Liu H, Das Sarma A. eTrust: Understanding trust evolution in an online world. In Proceedings of the ACM SIGKDD international conference on Knowledge discovery and data mining. 2012; pp. 253-261.

  37. Leskovec J, Huttenlocher D, Kleinberg J. Signed network in social media. In Proceedings of the SIGCHI conference on human factors in computing systems. 2010; pp. 1361-1370.

  38. Kumar S, Spezzano F, Subrahmanian VS, Faloutsos C. Edge weight prediction in weighted signed network. In IEEE International Conference on Data Mining (ICDM). 2016; pp. 221-230.

  39. Yang J, Leskovec J. Defining and evaluating network communities based on ground-truth. Knowl Info Syst. 2015;42(1):181–213.

    Article  Google Scholar 

  40. West R, Paskov HS, Leskovec J, Potts C. Exploiting social network structure for person-to-person sentiment analysis. Trans Assoc Comput Linguist. 2014;2:297–310.

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the State Key Program of National Nature Science Foundation of China (61936001), the National Key Research and Development Program of China (2016QY01W0200), partly funded by National Nature Science Foundation of China (61772096), the Key Research and Development Program of Chongqing (cstc2017zdcy-zdyfx0091), and the Key Research and Development Program on AI of Chongqing (cstc2017rgzn-zdyfx0022).

Author information

Authors and Affiliations

  1. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, P.R. China

    Qun Liu, Ying Chen, Gangqiang Zhang & Guoyin Wang

Authors
  1. Qun Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ying Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gangqiang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Guoyin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qun Liu.

Ethics declarations

Conflicts of interest

No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication. I would like to declare on behalf of my co-authors that the work described is original research. This work has not been published previously, and not under consideration for publication elsewhere, in whole or in part. All authors listed have approved the manuscript that is enclosed.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, Q., Chen, Y., Zhang, G. et al. A Novel Functional Network Based on Three-way Decision for Link Prediction in Signed Social Networks. Cogn Comput 14, 1942–1954 (2022). https://doi.org/10.1007/s12559-021-09873-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-021-09873-2

Keywords

Search

Navigation