Research Article
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Year 2025, Volume: 3 Issue: 1, 75 - 88, 27.06.2025
https://doi.org/10.71074/CTC.1721800

Abstract

References

  • E. K. Köse, A. Karci, Sosyal ağlarda güvenilir ve güvenilmez bireylerin tespit edilmesi, Computer Science (Special) (2021) 341–346.
  • E. Karadeniz, M. M. TEMEL, A. KARCI, Prediction of collaboration between universities of turkey, in: 2018 Interna- tional Conference on Artificial Intelligence and Data Processing (IDAP), IEEE, 2018, pp. 1–4.
  • M. U. Demirci, P. Karagoz, Trust modeling in recommendation: Explicit and implicit trust model compatibility and explicit trust prediction, in: Proceedings of the 13th International Conference on Management of Digital EcoSystems, 2021, pp. 8–14.
  • S. Hamdi, A. L. Gancarski, A. Bouzeghoub, S. B. Yahia, Tison: Trust inference in trust-oriented social networks, ACM Transactions on Information Systems (TOIS) 34 (3) (2016) 1–32.
  • J. A. Golbeck, Computing and applying trust in web-based social networks, University of Maryland, College Park, 2005.
  • C.Y. Lin, N. Cao, S. X. Liu, S. Papadimitriou, J. Sun, X. Yan, Smallblue: Social network analysis for expertise search and collective intelligence, in: 2009 IEEE 25th International Conference on Data Engineering, IEEE, 2009, pp. 1483– 1486.
  • P. Massa, P. Avesani, Trust metrics on controversial users: Balancing between tyranny of the majority, International Journal on Semantic Web and Information Systems (IJSWIS) 3 (1) (2007) 39–64.
  • H.K.Oh, J.W. Kim, S.W. Kim, K. Lee, A unified framework of trust prediction based on message passing, Cluster Computing 22 (2019) 2049–2061.
  • A. Josang, R. Hayward, S. Pope, Trust network analysis with subjective logic, in: Conference Proceedings of the Twenty- Ninth Australasian Computer Science Conference (ACSW 2006), Australian Computer Society, 2006, pp. 85–94.
  • R. Guha, R. Kumar, P. Raghavan, A. Tomkins, Propagation of trust and distrust, in: Proceedings of the 13th international conference on World Wide Web, 2004, pp. 403–412.
  • W. Jiang, G. Wang, J. Wu, Generating trusted graphs for trust evaluation in online social networks, Future generation computer systems 31 (2014) 48–58.
  • N. Fatehi, H. S. Shahhoseini, J. Wei, C.-T. Chang, An automata algorithm for generating trusted graphs in online social networks, Applied Soft Computing 118 (2022) 108475.
  • R. Goyal, A. K. Upadhyay, S. Sharma, P. K. Mishra, Analysis of predicting trust in complex online social networks, Materials Today: Proceedings 29 (2020) 573–580.
  • S. M. Ghafari, A. Beheshti, A. Joshi, C. Paris, A. Mahmood, S. Yakhchi, M. A. Orgun, A survey on trust prediction in online social networks, IEEE Access 8 (2020) 144292–144309.
  • Z. Htun, P. P. Tar, A trust-aware recommender system based on implicit trust extraction, Int. J. Innov. Eng. Tech- nol.(IJIET) Technol.(IJIET) 2 (2013) 271–276.
  • Y. Liu, C. Liang, F. Chiclana, J. Wu, A knowledge coverage-based trust propagation for recommendation mechanism in social network group decision making, Applied Soft Computing 101 (2021) 107005.
  • H. Mayadunna, L. Rupasinghe, A trust evaluation model for online social networks, in: 2018 National Information Technology Conference (NITC), IEEE, 2018, pp. 1–6.
  • T. Wu, R. Zhang, X. Liu, F. Liu, Y. Ding, A social commerce purchasing decision model with trust network and item review information, Knowledge-Based Systems 235 (2022) 107628.
  • M. Ghavipour, M. R. Meybodi, Stochastic trust network enriched by similarity relations to enhance trust-aware recom- mendations, Applied Intelligence 49 (2019) 435–448.
  • D. Canturk, P. Karagoz, S.-W. Kim, I. H. Toroslu, Trust-aware location recommendation in location-based social net- works: A graph-based approach, Expert Systems with Applications 213 (2023) 119048.
  • G. Jethava, U. P. Rao, A novel trust prediction approach for online social networks based on multifaceted feature simi- larity, Cluster Computing 25 (6) (2022) 3829–3843.
  • B. Yang, Y. Lei, J. Liu, W. Li, Social collaborative filtering by trust, IEEE transactions on pattern analysis and machine intelligence 39 (8) (2016) 1633–1647.
  • M. Kutay, S. Z. Dicle, M. U. Çaglayan, Çizge tabanlı güven modellenmesi, XIV. Akademik Bilişim Konferansı, 2012.
  • M. Ghavipour, M. R. Meybodi, Trust propagation algorithm based on learning automata for inferring local trust in online social networks, Knowledge-Based Systems 143 (2018) 307–316.
  • N. Biggs, E. K. Lloyd, R. J. Wilson, Graph Theory, 1736-1936, Oxford University Press, 1986.
  • I. Herman, G. Melanc¸on, M. S. Marshall, Graph visualization and navigation in information visualization: A survey, IEEE Transactions on visualization and computer graphics 6 (1) (2002) 24–43.
  • J. Chhugani, N. Satish, C. Kim, J. Sewall, P. Dubey, Fast and efficient graph traversal algorithm for cpus: Maximizing single-node efficiency, in: 2012 IEEE 26th International Parallel and Distributed Processing Symposium, IEEE, 2012, pp. 378–389.
  • S. Even, Graph algorithms, Cambridge University Press, 2011.
  • K. İnce, A. Karcı, Modelling and statistical analysis of academic collaborations as a new graph type, Journal of the Faculty of Engineering and Architecture of Gazi University 34 (1) (2019) 439–459.
  • S. Fortunato, Community detection in graphs, Physics reports 486 (3-5) (2010) 75–174.
  • G. Chartrand, Introductory graph theory, Courier Corporation, 2012.
  • K. Akilal, H. Slimani, M. Omar, A very fast and robust trust inference algorithm in weighted signed social networks using controversy, eclecticism, and reciprocity, Computers & Security 83 (2019) 68–78.

A NEW METHOD FOR DETERMINING THE TRUST STATUS OF INDIVIDUALS TO EACH OTHER IN SOCIAL NETWORKS

Year 2025, Volume: 3 Issue: 1, 75 - 88, 27.06.2025
https://doi.org/10.71074/CTC.1721800

Abstract

Social networks are a concept that has developed in recent years and have become widely used electronic environments in daily life. Social networks are modeled as graphs and problem solutions are handled graph-based. In this study, we seek to answer the questions “Does entity A trust entity B?” and “Which entity is trustworthy and which is not?” in a social network. In this study, the trust of one entity in the network towards another entity is calculated and expressed numerically. Classical trust inference algorithms eliminate paths with shortest paths, etc., which makes the found trust rate suspicious. In our method, all paths between two individuals are found and taken into account, which makes it unique. Keeping information safe from untrusted users is crucial for social network entities. The method aims to protect the privacy and confidentiality of the entities by detecting the trustworthiness of the entities. The proposed method is applied to standard social networks and the results are presented in this paper.

References

  • E. K. Köse, A. Karci, Sosyal ağlarda güvenilir ve güvenilmez bireylerin tespit edilmesi, Computer Science (Special) (2021) 341–346.
  • E. Karadeniz, M. M. TEMEL, A. KARCI, Prediction of collaboration between universities of turkey, in: 2018 Interna- tional Conference on Artificial Intelligence and Data Processing (IDAP), IEEE, 2018, pp. 1–4.
  • M. U. Demirci, P. Karagoz, Trust modeling in recommendation: Explicit and implicit trust model compatibility and explicit trust prediction, in: Proceedings of the 13th International Conference on Management of Digital EcoSystems, 2021, pp. 8–14.
  • S. Hamdi, A. L. Gancarski, A. Bouzeghoub, S. B. Yahia, Tison: Trust inference in trust-oriented social networks, ACM Transactions on Information Systems (TOIS) 34 (3) (2016) 1–32.
  • J. A. Golbeck, Computing and applying trust in web-based social networks, University of Maryland, College Park, 2005.
  • C.Y. Lin, N. Cao, S. X. Liu, S. Papadimitriou, J. Sun, X. Yan, Smallblue: Social network analysis for expertise search and collective intelligence, in: 2009 IEEE 25th International Conference on Data Engineering, IEEE, 2009, pp. 1483– 1486.
  • P. Massa, P. Avesani, Trust metrics on controversial users: Balancing between tyranny of the majority, International Journal on Semantic Web and Information Systems (IJSWIS) 3 (1) (2007) 39–64.
  • H.K.Oh, J.W. Kim, S.W. Kim, K. Lee, A unified framework of trust prediction based on message passing, Cluster Computing 22 (2019) 2049–2061.
  • A. Josang, R. Hayward, S. Pope, Trust network analysis with subjective logic, in: Conference Proceedings of the Twenty- Ninth Australasian Computer Science Conference (ACSW 2006), Australian Computer Society, 2006, pp. 85–94.
  • R. Guha, R. Kumar, P. Raghavan, A. Tomkins, Propagation of trust and distrust, in: Proceedings of the 13th international conference on World Wide Web, 2004, pp. 403–412.
  • W. Jiang, G. Wang, J. Wu, Generating trusted graphs for trust evaluation in online social networks, Future generation computer systems 31 (2014) 48–58.
  • N. Fatehi, H. S. Shahhoseini, J. Wei, C.-T. Chang, An automata algorithm for generating trusted graphs in online social networks, Applied Soft Computing 118 (2022) 108475.
  • R. Goyal, A. K. Upadhyay, S. Sharma, P. K. Mishra, Analysis of predicting trust in complex online social networks, Materials Today: Proceedings 29 (2020) 573–580.
  • S. M. Ghafari, A. Beheshti, A. Joshi, C. Paris, A. Mahmood, S. Yakhchi, M. A. Orgun, A survey on trust prediction in online social networks, IEEE Access 8 (2020) 144292–144309.
  • Z. Htun, P. P. Tar, A trust-aware recommender system based on implicit trust extraction, Int. J. Innov. Eng. Tech- nol.(IJIET) Technol.(IJIET) 2 (2013) 271–276.
  • Y. Liu, C. Liang, F. Chiclana, J. Wu, A knowledge coverage-based trust propagation for recommendation mechanism in social network group decision making, Applied Soft Computing 101 (2021) 107005.
  • H. Mayadunna, L. Rupasinghe, A trust evaluation model for online social networks, in: 2018 National Information Technology Conference (NITC), IEEE, 2018, pp. 1–6.
  • T. Wu, R. Zhang, X. Liu, F. Liu, Y. Ding, A social commerce purchasing decision model with trust network and item review information, Knowledge-Based Systems 235 (2022) 107628.
  • M. Ghavipour, M. R. Meybodi, Stochastic trust network enriched by similarity relations to enhance trust-aware recom- mendations, Applied Intelligence 49 (2019) 435–448.
  • D. Canturk, P. Karagoz, S.-W. Kim, I. H. Toroslu, Trust-aware location recommendation in location-based social net- works: A graph-based approach, Expert Systems with Applications 213 (2023) 119048.
  • G. Jethava, U. P. Rao, A novel trust prediction approach for online social networks based on multifaceted feature simi- larity, Cluster Computing 25 (6) (2022) 3829–3843.
  • B. Yang, Y. Lei, J. Liu, W. Li, Social collaborative filtering by trust, IEEE transactions on pattern analysis and machine intelligence 39 (8) (2016) 1633–1647.
  • M. Kutay, S. Z. Dicle, M. U. Çaglayan, Çizge tabanlı güven modellenmesi, XIV. Akademik Bilişim Konferansı, 2012.
  • M. Ghavipour, M. R. Meybodi, Trust propagation algorithm based on learning automata for inferring local trust in online social networks, Knowledge-Based Systems 143 (2018) 307–316.
  • N. Biggs, E. K. Lloyd, R. J. Wilson, Graph Theory, 1736-1936, Oxford University Press, 1986.
  • I. Herman, G. Melanc¸on, M. S. Marshall, Graph visualization and navigation in information visualization: A survey, IEEE Transactions on visualization and computer graphics 6 (1) (2002) 24–43.
  • J. Chhugani, N. Satish, C. Kim, J. Sewall, P. Dubey, Fast and efficient graph traversal algorithm for cpus: Maximizing single-node efficiency, in: 2012 IEEE 26th International Parallel and Distributed Processing Symposium, IEEE, 2012, pp. 378–389.
  • S. Even, Graph algorithms, Cambridge University Press, 2011.
  • K. İnce, A. Karcı, Modelling and statistical analysis of academic collaborations as a new graph type, Journal of the Faculty of Engineering and Architecture of Gazi University 34 (1) (2019) 439–459.
  • S. Fortunato, Community detection in graphs, Physics reports 486 (3-5) (2010) 75–174.
  • G. Chartrand, Introductory graph theory, Courier Corporation, 2012.
  • K. Akilal, H. Slimani, M. Omar, A very fast and robust trust inference algorithm in weighted signed social networks using controversy, eclecticism, and reciprocity, Computers & Security 83 (2019) 68–78.
There are 32 citations in total.

Details

Primary Language English
Subjects Computing Applications in Life Sciences
Journal Section Research Article
Authors

Esra Karadeniz Köse 0000-0002-7369-7211

Ali Karci

Early Pub Date June 25, 2025
Publication Date June 27, 2025
Submission Date June 17, 2025
Acceptance Date June 24, 2025
Published in Issue Year 2025 Volume: 3 Issue: 1

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