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Link prediction on signed social networks based on latent space mapping

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Abstract

Link prediction is an essential research area in social network analysis. In recent years, link prediction in signed networks has drawn much concentration of the researchers. To predict potential positive and negative links, we should predict not only the existence of the link between the nodes, but also the sign and the probability of the existence of the link. In addition, the link prediction result should satisfy the social balance and status theories as much as possible. In this paper, we propose an algorithm for link prediction in signed networks based on latent space mapping. Taking the social balance and status theories into consideration, we define a balance/status coefficient matrix to reflect the balance/status constrains on the signs of the unknown links. We also present the concept of signed degree ratio and the signed degree ratio-based similarity between the node pairs to measure probability of the signed links. We propose a latent space-based model for the connections in a signed network which combines the topological structure and the balance/status constrains. An alternative iteration algorithm is proposed to optimize the model, and its convergence and correctness are proved. By this alternative iteration method, time complexity of our algorithm is reduced greatly. Empirical results on real world signed networks demonstrate that the algorithm proposed can achieve higher quality predicting results than other algorithms.

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References

  1. Ahmed NM, Chen L (2016) An efficient algorithm for link prediction in temporal uncertain social networks. Inf Sci 331:120–136

    Article  MathSciNet  MATH  Google Scholar 

  2. Buccafurri F, Fotia L, Lax G et al (2016) Analysis-preserving protection of user privacy against information leak age of social-network Likes. Inf Sci 328:340–358

    Article  Google Scholar 

  3. Michał L J, Jaroszewicz S, Ostrowski Ł, Wierzbicki A (2016) Verifying social network models of Wikipedia knowledge community. Inf Sci 339:158–174

    Article  Google Scholar 

  4. Saito K, Kimura M, Ohara K, Motoda H (2016) Super mediator, A new centrality measure of node importance for information diffusion over social network. Inf Sci 329:985–1000

    Article  Google Scholar 

  5. Liao H, Zeng A, Zhang YC (2015) Predicting missing links via correlation between nodes. Phys A: Stat Mech Appl 436:216– 223

    Article  Google Scholar 

  6. Wang P, Xu BW, Wu YR, Zhou XY (2015) Link prediction in social networks: the state-of-the-art. Science China Inf Sci 58(1):1–38

    Google Scholar 

  7. Wang XM, Liu Y, Xiong F (2016) Improved personalized recommendation based on a similarity network. Phys A: Stat Mech Appl 456(15):271–280

    Article  Google Scholar 

  8. Buccafurri F, Lax G, Nocera A, Ursino D (2015) Discovering missing Me edges across social networks. Inf Sci 319:18–37

    Article  MathSciNet  MATH  Google Scholar 

  9. Fournet J, Barrat A (2014) Contact patterns among high school students. PLoS ONE 9(9):107878

    Article  Google Scholar 

  10. Ahmed NM, Chen L, Wang Y, Li B, Li Y, Liu W (2018) DeepEye: link prediction in dynamic networks based on non-negative matrix factorization. Big Data Min Anal 1(1):19–33

    Article  Google Scholar 

  11. Papadimitriou A, Symeonidis P, Manolopoulos Y (2012) Fast and accurate link prediction in social networking systems. J Syst Softw 85(9):2119–2132

    Article  Google Scholar 

  12. Sun Y, Barbery R, Gupta M, Aggarwal CC, Han J (2011) Co-author relationship prediction in multi-relational bibliographic networks. In: Proceedings of 2011 international conference on advances in social networks analysis and mining, (ASONAM 2011), pp 121–128

  13. Klimek P, Jovanovic AS, Egloff R, Schneider R (2016) Successful fish go with the flow: citation impact prediction based on centrality measures for term–document networks. Scientometrics 107(3):1265–1282

    Article  Google Scholar 

  14. Xie F, Chen Z, Shang JX, Feng XP, Li J (2015) A link prediction approach for item recommendation with complex number. Knowl-Based Syst 81:148–158

    Article  Google Scholar 

  15. Li J, Zhang LL, Meng F, Li FH (2014) Recommendation algorithm based on link prediction and domain knowledge in retail transactions. Procedia Comput Sci 31:875–881

    Article  Google Scholar 

  16. Li X, Chen HC (2013) Recommendation as link prediction in bipartite graphs: a graph kernel-based machine learning approach. Decis Support Syst 54(2):880–890

    Article  Google Scholar 

  17. Vidmer A, Zeng A, Medo M, Zhang YC (2015) Prediction in complex systems: the case of the international trade network. Physica A: Stat Mech Appl 436:188–199

    Article  Google Scholar 

  18. Kaya B, Poyraz M (2014) Supervised link prediction in symptom networks with evolving case. Measurement 56:231–238

    Article  Google Scholar 

  19. Ma X, Liao JL, Djouadi SM, Cao Q (2014) LIPS: link prediction as a service for data aggregation applications. Ad Hoc Netw 19: 43–58

    Article  Google Scholar 

  20. Song DJ, Meyer DA (2015) Link sign prediction and ranking in signed directed social networks. Soc Netw Anal Min 5(1):1–14

    Article  Google Scholar 

  21. Heider F (1946) Attitudes and cognitive organization. J Psychol 21(1):107–112

    Article  Google Scholar 

  22. Cartwright D, Harary F (1956) Structural balance: a generalization of Heider ’s theory. J Psychol Rev 63 (5):277–293

    Article  Google Scholar 

  23. 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. ACM, pp 403–412

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

    Article  Google Scholar 

  25. Ma H, Lyu MR, King I (2009) Learning to recommend with trust and distrust relationships. In: Proceedings of the third ACM conference on recommender systems. ACM, pp 189–196

  26. Victor P, Cornelis C, De Cock M, Teredesai A (2009) Trust and distrust-based recommendations for controversial reviews. In: Web science conference (WebSci’09: Society On-Line), number, p 161

  27. O’Donovan J, Smyth B (2005) Trust in recommender systems. In: Proceedings of the 10th international conference on intelligent user interface, pp 167–174

  28. Amin M Jalili J (2013) Cluster-based collaborative filtering for sign prediction in social networks with positive and negative links. In: ACM transaction on intelligent systems and technology

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

  30. Shahriary SR, Shahriari M, Noor R A community-based approach for link prediction in signed social networks. Sci Program 2015, Article ID 602690

  31. Chiang YK, Natarajan N, Tewari A, Dhillon IS (2011) Exploring longer cycles for link prediction in signed networks. ACM, Glasgow, pp 1157–1162

  32. Dubois T, Golbeck J, Srinivasan A (2011) Predicting Trust and Distrust in Social Networks. In: Privacy, security, risk and trust, 2011 IEEE the third international conference on social computing, pp 418–424

  33. Anchuri P, Magdon-Ismail M (2012) Communities and balance in signed networks: a spectral approach. In: IEEE/ACM international conference on advances in social networks analysis and mining

  34. Borzymek P, Sydow M (2010) Trust and distrust prediction in social network with combined graphical and review-based attributes. In: Agent and multi-agent systems: technologies and applications. Springer, Berlin, pp 122–131

  35. Ye J, Cheng H, Zhu Z et al (2013) Predicting positive and negative links in signed social networks by transfer learning. In: Proceedings of the 22nd international conference on the world wide web. ACM, New York, pp 1477–1488

  36. Zolfaghar K, Aghaie A (2010) Mining trust and distrust relations in social web applications. In: Proceedings of the 6th international conference on intelligent computer communication and processing. IEEE, pp 73–80

  37. Zheng X, Zeng D, Wang F (2015) Social balance in signed networks. Inf Syst Front 17(5):1077–1095

    Article  Google Scholar 

  38. 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 541–550

  39. Patidar A, Agarwal V, Bharadwaj KK (2012) Predicting friends and foes in signed social networks using inactive inference and social balance theory. IEEE, Piscataway, pp 384–388

  40. Javari A, Mahdi J (2014) Cluster based collaborative filtering for sign prediction in social networks with positive and negative links. ACM Trans Intell Syst Technol 5(2):1–24

    Article  Google Scholar 

  41. Symeonidies P, Eleftherios T (2014) Transitive node similarity: predicting and recommending links in signed social network. World Wide Web 17(4):743–776

    Article  Google Scholar 

  42. Lv LY, Zhou T (2011) Link prediction in complex networks: a survey. Physica A: Stat Mech Appl 390 (6):1150–1170

    Article  Google Scholar 

  43. Hsieh CJ, Chiang KY, Inderjit S (2012) Low rank modeling of signed networks. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge and data mining. ACM, New York, pp 507–515

  44. Yi C, Gu RT, Ji YF (2013) Sign inference for dynamic signed networks via dictionary learning. J Appl Math 2013(3):701–710

    MATH  Google Scholar 

  45. Priyanka A, Garg VK, Narayanam R (2013) Link prediction in signed social networks. In: Proceedings of the 23nd international conference on artificial intelligence. AAAI, Menlo Park, pp 2591–2597

  46. Ramanthan G, Kumar R, Raghavan P et al (2004) Propagation of trust and distrust. ACM, New York, pp 403–412

  47. Wang Y, Wang X, Zuo WL (2015) Research on trust prediction from a sociological perspective. J Comput Sci Technol 30(4):843–858

    Article  MathSciNet  Google Scholar 

  48. Tang JL, Chang Y, Aggarwal C, Liu H (2014) A survey of signed network mining in social media. ACM Comput Surv 9(4):Article 39. 36 pages

    Google Scholar 

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

  50. Lampe C, Johnston E, Resnick R (2007) Follow the reader: filtering comments on slashdot. In: Proceedings of the SIGCHI conference on human factors in computing systems, pp 1253–1262

  51. Burke M, Kraul R (2008) Mopping up: modeling Wikipedia promotion decisions. In: Proceedings of the ACM conference on computer supported cooperative work, pp 27–36

  52. Shahriari M, Jalili M (2014) Ranking nodes in signed social networks. Soc Netw Anal Min 4:article 172

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported in part by the Chinese National Natural Science Foundation under grant Nos. 61379066, 61702441, 61070047, 61379064, 61472344, 61402395, and 61602202; Natural Science Foundation of Jiangsu Province under contracts BK20130452, BK2012672, BK2012128, BK20140492 and Natural Science Foundation of Education Department of Jiangsu Province under contract 12KJB520019, 13KJB520026, 09KJB20013. Six talent peaks project in Jiangsu Province(Grant No. 2011-DZXX-032).

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Authors and Affiliations

  1. Department of Computer Science, Yangzhou University, Yangzhou, 225009, China

    Shensheng Gu, Ling Chen, Bin Li & Wei Liu

  2. State Key Lab of Novel Software Tech, Nanjing University, Nanjing, 210093, China

    Ling Chen & Bin Li

  3. Department of Computer Science, Huaiyin Institute of Technology, Huaiyin, 223001, China

    Bolun Chen

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  1. Shensheng Gu
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  2. Ling Chen
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  3. Bin Li
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  4. Wei Liu
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  5. Bolun Chen
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Correspondence to Ling Chen.

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Gu, S., Chen, L., Li, B. et al. Link prediction on signed social networks based on latent space mapping. Appl Intell 49, 703–722 (2019). https://doi.org/10.1007/s10489-018-1284-1

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