Link prediction in signed social networks based on fuzzy computational model of trust and distrust
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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.
KeywordsSigned social networks Trust Distrust Fuzzy Link prediction Social balance theory Positive links Negative links
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any one of the authors.
Informed consent was obtained from all the individual participants included in the study.
- Ahmad MA, Borbora Z, Srivastava J, Contractor N (2010) Link prediction across multiple social networks. In: Data mining workshops (ICDMW), IEEE international conference, pp 911–918Google Scholar
- 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 635–644Google Scholar
- Beigi G, Tang J, Liu H (2016) Signed link analysis in social media networks. In: ICWSM, pp 539–542Google Scholar
- 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 817–820Google Scholar
- 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 1157–1162Google Scholar
- 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):10Google Scholar
- Girdhar N, Bharadwaj KK (2016) Signed social networks: a survey. In: Proceedings of the international conference on advances in computing and data sciences, pp 326–335Google Scholar
- 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
- 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 403–412Google Scholar
- 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 analysisGoogle Scholar
- 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):24Google Scholar
- 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 85–94Google Scholar
- Kleinberg JM (2002) Small-world phenomena and the dynamics of information. In: Advances in neural information processing systems, pp 431–438Google Scholar
- 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 741–750Google Scholar
- 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 1361–1370Google Scholar
- 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. 641–650Google Scholar
- 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 384–388Google Scholar
- Patil AN (2009) Homophily based link prediction in social networks. Stony Brook University, Stony BrookGoogle Scholar
- Quercia D, Capra L (2009) FriendSensing: recommending friends using mobile phones. In: Proceedings of the third ACM conference on recommender systems, pp 273–276Google Scholar
- 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):70–80Google Scholar
- 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 87–96Google Scholar
- 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 831–835Google Scholar
- 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 555–564Google Scholar