Link prediction in signed social networks based on fuzzy computational model of trust and distrust

Abstract

Signed social networks are those in which users of the networks are connected with some interdependencies such as agreement/disagreement, liking/disliking, friends/foes, loving/despising, and companions/enemies. Most individuals in signed social networks have many relations in terms of friends, foes, following and followers. All these relations are usually asymmetric and subjective, thus difficult to predict. To resolve the fundamental problem of sparsity in the networks, substantial amount of research work has been dedicated to link prediction; however, very little work deals with the antagonistic behavior of the users while considering the asymmetric and domain-dependent nature of links. This paper is based on the concept that All Relations Are Not Equal and some relations are stronger than other relations. For instance some friends may be acquaintances of an individual, whereas another may be friends who care about him/her. In this paper, a fuzzy computational model is proposed based on trust and distrust, as a decision support tool that dissects relevant and reliable information of the users to distinguish the stronger relations from the weaker ones. Further, we have proposed two different link prediction models based on local information and local–global information to overcome the problem of sparsity in signed social networks. An extensive experimental study is performed on benchmarked synthetic dataset of friends and foes network and publicly available real-world datasets of Epinions and Slashdot. The results obtained are promising and establish the efficacy of our proposed models.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Notes

  1. 1.

    http://snap.stanford.edu/data/soc-sign-epinions.html.

  2. 2.

    http://snap.stanford.edu/data/soc-sign-Slashdot090221.html.

References

  1. Adamic LA, Adar E (2003) Friends and neighbors on the web. Soc Netw 25(3):211–230

    Article  Google Scholar 

  2. Agarwal V, Bharadwaj KK (2015) Predicting the dynamics of social circles in ego networks using pattern analysis and GA K-means clustering. Wiley Interdiscip Rev Data Min Knowl Discov 5(3):113–141

    Article  Google Scholar 

  3. Ahmad WNW, Ali NM (2018) A study on persuasive technologies: the relationship between user emotions, trust and persuasion. Int J Interact Multimed Artif Intell 5(1):57–61

    MathSciNet  Google Scholar 

  4. Ahmad MA, Borbora Z, Srivastava J, Contractor N (2010) Link prediction across multiple social networks. In: Data mining workshops (ICDMW), IEEE international conference, pp 911–918

  5. Anand D, Bharadwaj KK (2013) Pruning trust–distrust network via reliability and risk estimates for quality recommendations. Soc Netw Anal Min 3(1):65–84

    Article  Google Scholar 

  6. Awal GK, Bharadwaj KK (2014) Team formation in social networks based on collective intelligence—an evolutionary approach. Appl Intell 41(2):627–648

    Article  Google Scholar 

  7. Backstrom L, Leskovec J (2011) Supervised random walks: predicting and recommending links in social networks. In: Proceedings of the fourth ACM international conference on web search and data mining, pp 635–644

  8. Beigi G, Tang J, Liu H (2016) Signed link analysis in social media networks. In: ICWSM, pp 539–542

  9. Bharadwaj KK, Al-Shamri MYH (2009) Fuzzy computational models for trust and reputation systems. Electron Commer Res Appl 8(1):37–47

    Article  Google Scholar 

  10. Brzozowski MJ, Hogg T, Szabo G (2008) Friends and foes: ideological social networking. In: Proceedings of the SIGCHI conference on human factors in computing systems, pp 817–820

  11. Chiang KY, Natarajan N, Tewari A, Dhillon IS (2011) Exploiting longer cycles for link prediction in signed networks. In: Proceedings of the 20th ACM international conference on information and knowledge management, pp 1157–1162

  12. Davis D, Lichtenwalter R, Chawla NV (2013) Supervised methods for multi-relational link prediction. Soc Netw Anal Min 3(2):127–141

    Article  Google Scholar 

  13. Fire M, Tenenboim-Chekina L, Puzis R, Lesser O, Rokach L, Elovici Y (2013) Computationally efficient link prediction in a variety of social networks. ACM Trans Intell Syst Technol (TIST) 5(1):10

    Google Scholar 

  14. Girdhar N, Bharadwaj KK (2016) Signed social networks: a survey. In: Proceedings of the international conference on advances in computing and data sciences, pp 326–335

  15. Gong NZ, Talwalkar A, Mackey L, Huang L, Shin ECR, Stefanov E, Song D (2011) Jointly predicting links and inferring attributes using a social-attribute network (san). arXiv preprint arXiv:1112.3265

  16. Granovetter M (1983) The strength of weak ties: a network theory revisited. Soc Theory 1:201–233

    Article  Google Scholar 

  17. Guha R, Kumar R, Raghavan P, Tomkins A (2004) Propagation of trust and distrust. In: Proceedings of the 13th international conference on world wide web, pp 403–412

  18. Hangal S, MacLean D, Lam MS, Heer J (2010) All friends are not equal: using weights in social graphs to improve search. In: Proceedings of the 4th ACM workshop on social network mining and analysis

  19. Ibrahim NMA, Chen L (2015) Link prediction in dynamic social networks by integrating different types of information. Appl Intell 42(4):738–750

    Article  Google Scholar 

  20. Javari A, Jalili M (2014) Cluster-based collaborative filtering for sign prediction in social networks with positive and negative links. ACM Trans Intell Syst Technol (TIST) 5(2):24

    Google Scholar 

  21. Jøsang A, Hayward R, Pope S (2006) Trust network analysis with subjective logic. In: Proceedings of the 29th Australasian computer science conference, vol 48, pp 85–94

  22. Kant V, Bharadwaj KK (2013) Fuzzy computational models of trust and distrust for enhanced recommendations. Int J Intell Syst 28(4):332–365

    Article  Google Scholar 

  23. Kashoob S, Caverlee J (2012) Temporal dynamics of communities in social bookmarking systems. Soc Netw Anal Min 2(4):387–404

    Article  Google Scholar 

  24. Kleinberg JM (2002) Small-world phenomena and the dynamics of information. In: Advances in neural information processing systems, pp 431–438

  25. Kunegis J, Lommatzsch A, Bauckhage C (2009) The Slashdot zoo: mining a social network with negative edges. In: Proceedings of the 18th international conference on world wide web, pp 741–750

  26. Kutty S, Nayak R, Chen L (2014) A people-to-people matching system using graph mining techniques. World Wide Web 17(3):311–349

    Article  Google Scholar 

  27. Leskovec J, Huttenlocher D, Kleinberg J (2010a) Signed networks in social media. In: Proceedings of the SIGCHI conference on human factors in computing systems, pp 1361–1370

  28. Leskovec J, Huttenlocher D, Kleinberg J (2010b) Predicting positive and negative links in online social networks. In: Proceedings of the 19th international conference on world wide web, pp. 641–650

  29. Liben-Nowell D, Kleinberg J (2007) The link-prediction problem for social networks. J Assoc Inf Sci Technol 58(7):1019–1031

    Article  Google Scholar 

  30. Patidar A, Agarwal V, Bharadwaj KK (2012) Predicting friends and foes in signed networks using inductive inference and social balance theory. In: Proceedings of the international conference on advances in social networks analysis and mining (ASONAM), pp 384–388

  31. Patil AN (2009) Homophily based link prediction in social networks. Stony Brook University, Stony Brook

    Google Scholar 

  32. Peters S, Jacob Y, Denoyer L, Gallinari P (2012) Iterative multi-label multi-relational classification algorithm for complex social networks. Soc Netw Anal Min 2(1):17–29

    Article  Google Scholar 

  33. Quercia D, Capra L (2009) FriendSensing: recommending friends using mobile phones. In: Proceedings of the third ACM conference on recommender systems, pp 273–276

  34. Reguieg S, Taghezout N (2017) Supporting multi-agent coordination and computational collective intelligence in enterprise 2.0 platform. Int J Interact Multimed Artif Intell 4(6):70–80

    Google Scholar 

  35. Tang J, Chang S, Aggarwal C, Liu H (2015) Negative link prediction in social media. In: Proceedings of the eighth ACM international conference on web search and data mining, pp 87–96

  36. Tang J, Chang Y, Aggarwal C, Liu H (2016) A survey of signed network mining in social media. ACM Comput Surv (CSUR) 49(3):42

    Article  Google Scholar 

  37. Xie X (2010) Potential friend recommendation in online social network. In: Proceedings of the international conference on green computing and communications (GreenCom), & on cyber, physical and social computing (CPSCom), pp 831–835

  38. Yang SH, Smola AJ, Long B, Zha H, Chang Y (2012) Friend or frenemy? Predicting signed ties in social networks. In: Proceedings of the 35th international conference on research and development in information retrieval, pp 555–564

  39. Yang X, Guo Y, Liu Y (2013) Bayesian-inference-based recommendation in online social networks. IEEE Trans Parallel Distrib Syst 24(4):642–651

    Article  Google Scholar 

  40. Zhang K, Lo D, Lim EP, Prasetyo PK (2013) Mining indirect antagonistic communities from social interactions. Knowl Inf Syst 35(3):553–583

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Nancy Girdhar.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standard

This article does not contain any studies with human participants or animals performed by any one of the authors.

Informed consent

Informed consent was obtained from all the individual participants included in the study.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Communicated by V. Loia.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Girdhar, N., Minz, S. & Bharadwaj, K.K. Link prediction in signed social networks based on fuzzy computational model of trust and distrust. Soft Comput 23, 12123–12138 (2019). https://doi.org/10.1007/s00500-019-03768-z

Download citation

Keywords

  • Signed social networks
  • Trust
  • Distrust
  • Fuzzy
  • Link prediction
  • Social balance theory
  • Positive links
  • Negative links