Volume 18, Issue 2 (10-2021)                   JSDP 2021, 18(2): 57-74 | Back to browse issues page

XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Abbaspour orangi M, Hashemi golpayegani A. Identifying Influential Nodes to Diffuse the Trusting Behavior in Social Networks. JSDP. 2021; 18 (2) :57-74
URL: http://jsdp.rcisp.ac.ir/article-1-967-en.html
Department of Computer Engineering and Information Technology, Amirkabir University of Technology
Abstract:   (566 Views)
Trust is one of the most important cornerstones in social networks' discussions. mostly the way that users of these networks trust each other are considered identical, while these users can have different approaches and considerations in trusting others. Meanwhile, users can impress each other and change their trusting patterns in other users. As a result, the mechanism and manner of impressing opinion trust behavior and conditions of behavioral modes changing have a place of importance to be considered. The question is that, how we can consider different behavior of users and their impression in trusting others? In the first step, the main purpose of this paper is to spotlight social networks' different user behavior in trusting other users. For this purpose, the three most important behavioral modes in  users trust are considered. In each of these modes, behavioral and functional characteristics of users are the basis of calculating trust, which is based on mental beliefs of them. These modes are named as optimistic, moderate, and pessimistic trusting modes. In optimistic mode, we suppose that users think positively and consider low level of activities and signs in trusting others. Here, negative interactions have little impact on users mind. In moderate mode, we suppose that users are not as optimistic as mode A and consider all the interactions and signs when they want to trust others. Here, any negative action can destroy the trust of users and has a greater impact on users. Finally, in pessimistic mode, we suppose that users are pessimistic and hardly trust someone. In this mode, the interactions that happened more recently have more value than those happened in the past.
In the next step, the purpose and innovation of this paper is the way that the trust behavior of users spreads. Three different scenarios are considered for the impressing and spreading of nodes behavior, purposely. In each scenario, different states for users and different purposes for diffusion are defined.   Next, it is followed by maximizing of impression and finding more impressive agents in diffusing trust behavior through social networks. For this purpose, it's focused on the structure of users social networks, and the most impressive ones are determined through different diffusion scenarios. The findings of this article appear a significant discrepancy in the amount of trust in each of the different behavioral modes, which is more acceptable in the real world. Analyzing test results leads us to the fact that in the presented model, choosing the start node from each community with 48.14 percent in behavior improvement and diffusion speed and the nodes with the highest degree with 37.03 percent in behavior changing has much more reasonable results than usual models.
Full-Text [PDF 977 kb]   (157 Downloads)    
Type of Study: Research | Subject: Paper
Received: 2019/02/4 | Accepted: 2021/01/10 | Published: 2021/10/8 | ePublished: 2021/10/8

References
1. [1] J. Barnes, "Class and committees in a Norwegian island parish," in Human relations, 7, no.1, 1954, pp. 39-58. [DOI:10.1177/001872675400700102]
2. [2] J. A. Golbeck, "Computing and applying trust in web-based social networks," Doctoral dissertation, 2005.
3. [3] E. Uslaner, "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," no. October, 2017.
4. [4] S. Nepal, W. Sherchan and C. Paris, "Strust: a trust model for social networks", in Trust, Security, and Privacy in Computing and Communications (TrustCom), 2011 IEEE 10th International Conference on. IEEE, 2011, pp. 841-846. [DOI:10.1109/TrustCom.2011.112]
5. [5] S. A. Hashemi Golpayegani, L. Esmaeili, S. Mardani, S. M. Mutallebi, "A survey of trust in social commerce" Latest Trends of E-Systems: concept, development and applications, Apple Academic Press, Vol. 1, 2015, pp. 3-40. [DOI:10.1201/9781315366593-3]
6. [6] S. Nepal, S. K. Bista, and C. Paris, "Behavior-based propagation of trust in social networks with restricted and anonymous participation," Comput. Intell., vol. 31, no. 4, pp. 642-668, 2015. [DOI:10.1111/coin.12041]
7. [7] D. Centola, "The spread of behavior in an online social network experiment", in science 329, no 5996, 2010, pp. 1194-1197. [DOI:10.1126/science.1185231] [PMID]
8. [8] D. Centola, M. Eguı, and M. W. Macy, "Cascade dynamics of complex propagation," Physica A: Statistical Mechanics and its Applications, vol. 374, pp. 449-456, 2007. [DOI:10.1016/j.physa.2006.06.018]
9. [9] D. Chen, L. Lü, M. S. Shang, Y. C. Zhang, and T. Zhou, "Identifying influential nodes in complex networks," Physica a: Statistical mechanics and its applications, vol. 391, no. 4, pp. 1777-1787, 2012. [DOI:10.1016/j.physa.2011.09.017]
10. [10] S. Ahmed and C. I. Ezeife, "Discovering influential nodes from trust network," In Proceedings of the 28th Annual ACM Symposium on Applied Computing, pp. 121-128, 2013. [DOI:10.1145/2480362.2480389]
11. [11] I. G. Amnieh and M. Kaedi, "Using estimated personality of social network members for finding influential nodes in viral marketing," Cybernetics and Systems, vol. 46, no. 5, pp. 355-378, 2015. [DOI:10.1080/01969722.2015.1029769]
12. [12] Y. Zhang, Z. Wang, and C. Xia, "Identifying key users for targeted marketing by mining online social network," In 2010 IEEE 24th International Conference on Advanced Information Networking and Applications Workshops., 2010, pp. 644-649. [DOI:10.1109/WAINA.2010.137]
13. [13] M. Jang, C. Faloutsos, S. Kim, U. Kang, and J. Ha, "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, 2016, pp. 629-638. [DOI:10.1145/2983323.2983753]
14. [14] S. Adali, et al, "Measuring behavioral trust in social networks", in Intelligence and Security Informatics (ISI), 2010 IEEE International Conference. IEEE, 2010, pp. 150-152. [DOI:10.1109/ISI.2010.5484757]
15. [15] C. Hang, Z. Zhang and M. P. Singh, "Shin: generalized trust propagation with limited evidence", in Computer, vol. 46, no.3, 2013, pp. 78-85. [DOI:10.1109/MC.2012.116]
16. [16] T. Švec and J. Samek, "Trust evaluation on Facebook using multiple contexts", in 21st Conference on User Modeling, Adaptation, and Personalization, 2013, pp. 1-10.
17. [17] L. Esmaeili, M. Mutallebi, S. Mardani, and S. A. H. Golpayegani, "Studying the affecting factors on trust in social commerce", in International Journal of advanced studies in Computer Science and Engineering, vol. 4, no. 6, pp. 41-47, 2015.
18. [18] Y. Kim and H. Song, "Strategies for predicting local trust based on trust propagation in social networks", Knowledge-Based Systems, vol. 24, no. 8, pp. 1360-1371, 2011. [DOI:10.1016/j.knosys.2011.06.009]
19. [19] S.U. Nasir, & T. H. Kim, "Trust Computation in Online Social Networks Using Co-Citation and Transpose Trust Propagation", IEEE Access, vol. 8, pp. 41362-41371, 2020. [DOI:10.1109/ACCESS.2020.2975782]
20. [20] S. Agreste, P. De Meo, E. Ferrara, S. Piccolo, and A. Provetti, "Trust networks: topology, dynamics and measurements", in IEEE Internet Computing 19, no. 6, 2015, pp. 26-35. [DOI:10.1109/MIC.2015.93]
21. [21] R. Urena, G. Kou, Y. Dong, F. Chiclana, and E. Herrera-Viedma. "A review on trust propagation and opinion dynamics in social networks and group decision making frameworks," Information Sciences, no. 478, pp. 461-475, 2019. [DOI:10.1016/j.ins.2018.11.037]
22. [22] M. Kimura, K. Saito, R. Nakano, and H. Motoda, "Extracting influential nodes on a social network for information diffusion," Data Mining and Knowledge Discovery, vol. 20, no. 1, pp. 70-97, 2010 [DOI:10.1007/s10618-009-0150-5]
23. [23] X. Wang, Y. Su, C. Zhao, and D. Yi, "Effective identification of multiple influential spreaders by DegreePunishment," Physica A: Statistical Mechanics and its Applications, vol. 461, pp. 238-247, 2016. [DOI:10.1016/j.physa.2016.05.020]
24. [24] K. Tanınmış, N. Aras, & K. I. Altınel, "Influence maximization with deactivation in social networks", European Journal of Operational Research, vol. 278(1), pp. 105-119, 2019. [DOI:10.1016/j.ejor.2019.04.010]
25. [25] H. M. Wagih, H. M. Mokhtar, and S. S. Ghoniemy, "Exploring Trusted Relations among Virtual Interactions in Social Networks for Detecting Influence Diffusion," ISPRS International Journal of Geo-Information, vol. 8, no. 9, pp. 415, 2019. [DOI:10.3390/ijgi8090415]
26. [26] M. Hosseini-Pozveh, K. Zamanifa, & A. Naghsh-Nilchi, "Assessing information diffusion models for influence maximization in signed social networks", Expert Systems with Applications, no.119, pp. 476-490, 2019. [DOI:10.1016/j.eswa.2018.07.064]
27. [27] B. Zhang, L. Zhang, C. Mu, Q. Zhao, Q. Song, and X. Hong, "A most influential node group discovery method for influence maximization in social networks: a trust-based perspective," Data & Knowledge Engineering, no. 121, pp.71-87, 2019. [DOI:10.1016/j.datak.2019.05.001]
28. [28] N. Wang, J. Da, J. Li, and Y. Liu, "Influence maximization with trust relationship in social networks'', In 2018 14th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) 2018, December, pp. 61-67. IEEE. [DOI:10.1109/MSN.2018.00017]
29. [29] M. Golembiewski, R. T., & McConkie, "The centrality of interpersonal trust in group processes," Theories of group processes, vol. 131, p. 185, 1975.
30. [30] P. Dey, A. Chaterjee, & S. Roy, " Influence maximization in online social network using different centrality measures as seed node of information propagation ", Sādhanā, vol. 44(9), pp. 205, Chicago, 2019. [DOI:10.1007/s12046-019-1189-7]
31. [31] T. Lappas, E. Terzi, D. Gunopulos, and H. Mannila, "Finding effectors in social networks," In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, 2010, pp. 1059-1068. [DOI:10.1145/1835804.1835937]
32. [32] Y. Zhang, Z. Wang, and C. Xia, "Identifying key users for targeted marketing by mining online social network," In 2010 IEEE 24th International Conference on Advanced Information Networking and Applications Workshops. WAINA 2010, pp. 644-649. [DOI:10.1109/WAINA.2010.137]
33. [33] D. B. Chen, H. Gao, L. Lü, and T. Zhou, "Identifying influential nodes in large-scale directed networks: The role of clustering," PLoS One, vol. 8, no. 10, pp. 1-10, 2013. [DOI:10.1371/journal.pone.0077455] [PMID] [PMCID]
34. [34] T. Martin, X. Zhang, and M. E. J. Newman, "Localization and centrality in networks," Physical review E, vol. 90, no. 5, pp.052808, 2014. [DOI:10.1103/PhysRevE.90.052808] [PMID]
35. [35] Z. Yang, R. Algesheimer, and C. J. Tessone, "OPEN a comparative analysis of community detection algorithms on artificial networks," Scientific Reports, no. 6, pp.30750. July, 2016. [DOI:10.1038/srep30750] [PMID] [PMCID]
36. [36] M. De Domenico, A. Lima, P. Mouge and M. Musolesi, "The anatomy of a scientific rumor", In Scientific reports, no.3, pp.2980, 2013. [DOI:10.1038/srep02980] [PMID] [PMCID]
37. [37] M. DEUTSCH, "Cooperation and trust. some theoretical notes," in Jones, M.R. (ed) Nebraska Symposium on Motivation. Nebraska University Press, 1962.

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2015 All Rights Reserved | Signal and Data Processing