Finding effective nodes to maximize the trusting behavior propagation in social networks

Abstract

It is generally accepted that trust is one of the main issues in the area of social networks. Trust values can be measured explicitly or implicitly. There are various approaches to compute the trust value of users implicitly. These approaches consider a uniform trusting behavior for all users of a social network. While it would be more accurate to acknowledge the differences between the users in terms of how they trust others and the factors that they consider in this regard. Trusting behavior of users can also be influenced by the behavior of others with whom they interact. The mechanism of these influences and the conditions for changing the trusting behavior are of great importance for this discussion. In this study, our goal is to model the differences between the trusting behaviors of social network users. For this purpose, we define three behavior modes for the way users trust each other. In each mode, trust calculations are based on behavioral and functional characteristics of users, which are shaped by their subjective beliefs. A dataset that has the interaction information between each pair of nodes is used to define the trusting behavior pattern between users. Also, three scenarios are defined for assessing the propagation of the behavior modes. Then, we attempt to maximize the influence and find the effective nodes for propagating the trusting behavior modes in the network. To this end, we focus on the structure of the social network users in different propagation scenarios. The results show differences between the trust level outcomes reached with each behavior mode. Also, the impacts of propagation source node selection methods differ in each propagation scenario.

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References

  1. 1.

    Barnes J (1954) Class and committees in a Norwegian island parish. Hum Relat 7(1):39–58

    Article  Google Scholar 

  2. 2.

    Golbeck JA (2005) Computing and applying trust in web-based social networks. Doctoral (dissertation)

  3. 3.

    Nepal S, Sherchan W, Paris C (2011) Strust: a trust model for social networks. In: 2011 IEEE 10th international conference on trust, security, and privacy in computing and communications (TrustCom). IEEE, pp 841–846

  4. 4.

    Uslaner E (2017) The moral foundation of trust the moral foundations of trust * Eric M . Uslaner Department of Government and Politics University of Maryland—College Park College Park , MD 20742 Prepared for the Symposium, “Trust in the Knowledge Society”, University of Jyvaskyla, Jyvaskala, Finland, 20 September, 2002 and for presentation at Nuffield College, Oxford

  5. 5.

    Singh S, Bawa S (2007) A privacy, trust and policy based authorization framework for services in distributed environments. Int J Comput Sci 2(2):85–92

    Google Scholar 

  6. 6.

    Hashemi Golpayegani SA, Esmaeili L, Mardani S, Mutallebi SM (2015) A survey of trust in social commerce, latest trends of E-systems: concept, development and applications, vol 1. Apple Academic Press, pp 3–40

  7. 7.

    Nepal S, Bista SK, Paris C (2015) Behavior-based propagation of trust in social networks with restricted and anonymous participation. Comput Intell 31(4):642–668

    MathSciNet  Article  Google Scholar 

  8. 8.

    Centola D (2010) The spread of behavior in an online social network experiment. Science 329(5996):1194–1197

    Article  Google Scholar 

  9. 9.

    Centola D, Eguı M, Macy MW (2007) Cascade dynamics of complex propagation. Phys A Stat Mech Its Appl 374:449–456

    Article  Google Scholar 

  10. 10.

    Christakis NA, Fowler JH (2008) The collective dynamics of smoking in a large social network. N Engl J Med 6:2249–2258

    Article  Google Scholar 

  11. 11.

    Centola D, Macy M (2007) Complex contagions and the weakness of long ties. Am J Sociol 113(3):702–734

    Article  Google Scholar 

  12. 12.

    Chen D, Lü L, Shang MS, Zhang YC, Zhou T (2012) Identifying influential nodes in complex networks. Phys A Stat Mech Its Appl 391(4):1777–1787

    Article  Google Scholar 

  13. 13.

    Ahmed S, Ezeife CI (2013) Discovering influential nodes from trust network. In: Proceedings of the 28th annual ACM symposium on applied computing, pp 121–128

  14. 14.

    Zhu Z (2013) Discovering the influential users oriented to viral marketing based on online social networks. Phys A Stat Mech Its Appl 392(16):3459–3469

    MathSciNet  Article  Google Scholar 

  15. 15.

    Amnieh IG, Kaedi M (2015) Using estimated personality of social network members for finding influential nodes in viral marketing. Cybern Syst 46(5):355–378

    Article  Google Scholar 

  16. 16.

    Zhang Y, Wang Z, Xia C (2010) Identifying key users for targeted marketing by mining online social network. In: 2010 IEEE 24th international conference on advanced information networking and applications workshops, pp 644–649

  17. 17.

    Jang M, Faloutsos C, Kim S, Kang U, Ha J (2016) Pin-Trust: fast trust propagation exploiting positive, implicit, and negative information. In: Proceedings of the 25th ACM international on conference on information and knowledge management. ACM, pp 629–638

  18. 18.

    Adali S et al (2010) Measuring behavioral trust in social networks. In: 2010 IEEE international conference intelligence and security informatics (ISI). IEEE, pp 150–152

  19. 19.

    Hang C, Zhang Z, Singh MP (2013) Shin: generalized trust propagation with limited evidence. Computer 46(3):78–85

    Article  Google Scholar 

  20. 20.

    Švec T, Samek J (2013) Trust evaluation on Facebook using multiple contexts. In: 21st Conference on user modeling, adaptation, and personalization, pp 1–10

  21. 21.

    Esmaeili L, Mutallebi M, Mardani S, Golpayegani SAH (2015) Studying the affecting factors on trust in social commerce. Int J Adv Stud Comput Sci Eng 4(6):41–47

    Google Scholar 

  22. 22.

    Kim Y, Song H (2011) Strategies for predicting local trust based on trust propagation in social networks. Knowl Based Syst 24(8):1360–1371

    Article  Google Scholar 

  23. 23.

    Luca Allodi LC, Marco C (2011) The asymmetric diffusion of trust between communities: simulations in dynamic social networks. In: Proceedings of the winter simulation conference, pp 3146–3157

  24. 24.

    Easa FR, Ghaemi Bafghi A, Shakeri H (2012) A group-based trust propagation method. In: 2nd international eConference on computer and knowledge engineering (ICCKE). IEEE, pp 313–317

  25. 25.

    Agreste S, De Meo P, Ferrara E, Piccolo S, Provetti A (2015) Trust networks: topology, dynamics and measurements. IEEE Internet Comput 19(6):26–35

    Article  Google Scholar 

  26. 26.

    Kimura M, Saito K, Nakano R, Motoda H (2010) Extracting influential nodes on a social network for information diffusion. Data Min Knowl Discov 20(1):70–97

    MathSciNet  Article  Google Scholar 

  27. 27.

    Wang X, Su Y, Zhao C, Yi D (2016) Effective identification of multiple influential spreaders by DegreePunishment. Phys A Stat Mech Its Appl 461:238–247

    Article  Google Scholar 

  28. 28.

    Li Y, Cao H, Zhang Y, Li B (2018) Characteristics of human behavior in an online society. SAGE Open 8(2):2158244018770494

    Article  Google Scholar 

  29. 29.

    Abbaspour Orangi M, Hashemi Golpayegani A (2018) An activity-based user trusting behavior diffusion model in social networks. In: 2018 9th International symposium on telecommunications (IST). IEEE, pp 32–38

  30. 30.

    Golembiewski M, McConkie RT (1975) The centrality of interpersonal trust in group processes. Theor Group Process 131:185

    Google Scholar 

  31. 31.

    Deutsch M (1962) Cooperation and trust, some theoretical notes. In: Jones MR (ed) Nebraska symposium on motivation. Nebraska University Press

  32. 32.

    Lappas T, Terzi E, Gunopulos D, Mannila H (2010) Finding effectors in social networks. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 1059–1068

  33. 33.

    Chen DB, Gao H, Lü L, Zhou T (2013) Identifying influential nodes in large-scale directed networks: the role of clustering. PLoS ONE 8(10):1–10

    Google Scholar 

  34. 34.

    Wang Y, Cong G, Song G, Xie K (2010) Community-based Greedy algorithm for mining Top-K influential nodes in mobile social networks categories and subject descriptors. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 1039–1048

  35. 35.

    Yang Z, Algesheimer R, Tessone CJ (2016) OPEN a comparative analysis of community detection algorithms on artificial networks. Sci Rep 6:30750

    Article  Google Scholar 

  36. 36.

    De Domenico M, Lima A, Mouge P, Musolesi M (2013) The anatomy of a scientific rumor. Sci Rep 3:2980

    Article  Google Scholar 

  37. 37.

    Zhang S, Zhong H (2019) Mining users trust from E-commerce reviews based on sentiment similarity analysis. IEEE Access 7:13523–13535

    Article  Google Scholar 

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Correspondence to Alireza Hashemi Golpayegani.

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Abbaspour Orangi, M., Hashemi Golpayegani, A. Finding effective nodes to maximize the trusting behavior propagation in social networks. Computing (2021). https://doi.org/10.1007/s00607-021-00949-3

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Keywords

  • Social network analysis
  • Behavior diffusion
  • Trust behavior
  • Trust propagation

Mathematics Subject Classification

  • 68R10