Skip to main content
SpringerLink
Log in

TRTCD: trust route prediction based on trusted community detection

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Social networks have become increasingly popular and are used for various activities. It is essential to evaluate the trustworthiness of the path between two unknown users in social networks. However, there are usually many social routes between them. In applications of trust, confidence relations among users need to be predicted. Trust route prediction predicts a new trust relationship between two users who are not currently connected. Thus, a challenging problem is finding which social trust route is optimal to yield the most trustworthy route. As a result, this process faces many challenges, such as the sparsity of user-specified trust relations, context awareness of trust, and changes in trust values over time. A new trust route prediction framework was proposed in this paper to enhance prediction accuracy. Considering community relations and node information for community detection, the proposed trust route prediction algorithm, TRTCD, is introduced. The effect of node and community information on link prediction accuracy was empirically investigated here using seven parameters. Experiments on eleven real-world datasets showed that the proposed method performed better than the fourteen existing methods. Based on the obtained experimental results, the proposed method performs better than other methods regarding accuracy and cost. The results show that the TRTCD has, on average, 22% better on eight directed datasets. The results of the NDCG measure show that the TRTCD can reach the average value of trust very close to 1, which is outstanding and performs better than other algorithms in terms of the ATCE criterion since more trusted and integrated communities are identified. In addition, the results show that the TRTCD can be successfully used in directed social networks but needs to work better in undirected social networks.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Notes

  1. http://konect.uni-koblenz.de/ and https://snap.stanford.edu/

References

  1. Adali S, Escriva R, Goldberg MK, Hayvanovych M (2010) Measuring behavioral trust in social networks, Conference: Intelligence and Security Informatics (ISI), IEEE, p 105

  2. Adamic L, Adar E (2003) Friends and neighbors on the Web. Soc Netw 25(3):211

    Google Scholar 

  3. Albert R, Barabasi AL (2002) Statistical mechanics of complex networks. Rev Modern Physics 74(1):47–97

  4. Azadjalal MM, Moradi P, Abdollahpouri A, Jalili M (2017) A trustaware recommendation method based on pareto dominance and confidence concepts. Knowl-Based Syst 116:130–143

    Google Scholar 

  5. Bai S, Li L, Cheng J, Xu S, Chen X (2018) Predicting missing links based on a new triangle structure. Complexity 2018:1

    Google Scholar 

  6. Barabási AL, Albert R (1999) Emergence of Scaling in Random Networks. Sci 286(5439):509

    MathSciNet  MATH  Google Scholar 

  7. Belkhadir I, Didi Omar E, Boumhidi J (2019) An intelligent recommender system using social trust path for recommendations in web-based social networks. Procedia Comput Sci 148:181

    Google Scholar 

  8. Belkin M, Niyogi P (2001) Laplacian eigenmaps and spectral techniques for embedding and clustering, Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic, p 585

  9. Biswas A, Biswas B (2015) Investigating community structure in perspective of ego network. Expert Syst 42(20):6913

    Google Scholar 

  10. Biswas A, Biswas B (2017) Community-based link prediction. Multimed Tools Appl 76(18):619

    Google Scholar 

  11. Biswas A, Biswas B (2017) Analyzing evolutionary optimization and community detection algorithms using regression line dominance. Inf Sci 396:185–201

    MATH  Google Scholar 

  12. Biswas A, Biswas B (2018) Fuzag: fuzzy agglomerative community detection by exploring the notion of self-membership. IEEE Trans Fuzzy Syst 26(5):2568

    Google Scholar 

  13. Boccaletti S, Latora V, Moreno Y, Chavez M (2006) Complex networks: Structure and dynamics. Phys Rep 424(4):175

    MathSciNet  MATH  Google Scholar 

  14. Caiyan D, Chen L, Li B (2017) Link prediction in complex network based on modularity. Soft Comput 21(15):4197

    Google Scholar 

  15. Cannistraci CV, Alanis-Lobato G, Ravasi T (2013) From link-prediction in brain connectomes and protein interactomes to the local-communityparadigm in complex networks. Sci Rep 3(1):1613

    Google Scholar 

  16. Chaoji V, Ranu S, Rastogi R, Bhatt R (2012) Recommendations to boost content spread in social networks, Proceedings of the 21st International Confer- ence on World Wide Web, p 529

  17. Chen X, Xia C, Wang J (2018) A novel trust-based community detection algorithm used in social networks. Chaos, Solitons Fractals 108:57

  18. Cheng J, Leng M, Li L, Zhou H, Chen X (2014) Active Semi-Supervised Community Detection Based on Must-Link and Cannot-Link Constraints. Plos One, vol. 9, no. 10

  19. Clauset A, Newman MEJ, Moore C (2004) Finding community structure in very large networks. Phys Rev E. vol. 70, no. 6

  20. Clauset A, Moore C, Newman MEJ (2008) Hierarchical structure and the prediction of missing links in networks. Nature 453(7191):98

    Google Scholar 

  21. de Sa HR, Prudencio RBC (2011) Supervised link prediction in weighted networks, The 2011 International Joint Conference on Neural Networks. IEEE, p 2281

  22. Deylami HA, Asadpour M (2015) Link prediction in social networks using hierarchical community detection. Proc 7th Conf Inf Knowl Technol (IKT), Urmia, pp 1–5. https://doi.org/10.1109/IKT.2015.7288742

  23. Dick K, Green JR (2018) Reciprocal perspective for improved protein-protein interaction prediction. Sci Rep 8:11694. https://doi.org/10.1038/s41598-018-30044-1

    Article  Google Scholar 

  24. Ding J, Jiao L, Wu J, Hou Y, Qi Y (2015) Prediction of missing links based on multi-resolution community division. Physica A 417:76–85

  25. Feng X, Zhao JC, Xu K (2012) Link prediction in complex networks: A clustering perspective. Eur Phys J 85(1):3

    Google Scholar 

  26. Feng S, Shen D, Nie T, Yue K, He J, Ge Y (2018) Inferring anchor links based on social network structure. IEEE Access 6:17340

    Google Scholar 

  27. Feyessa T, Bikdash M, Lebby G (2011) Node-pair feature extraction for link prediction, Proceedings - 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT/SocialCom, p. 1421

  28. Fire M, Tenenboim L, Lesser O, Puzis R, Rokach L, Elovici Y (2012) Link prediction in social networks using computationally efficient topological features. IEEE Third Int Conf Privacy, Secur Risk Trust, Boston, pp 73–80. https://doi.org/10.1109/PASSAT/SocialCom.2011.20

  29. Forouzandeh S, Soltanpanah H, Sheikhahmadi A (2015) Application of data mining in designing a recommender system on social, International Journal of Computer Applications, vol. 124, no. 1

  30. Forouzandeh S, Sheikhahmadi A, Rezaei Aghdam A, Xu S (2018) New centrality measure for nodes based on user social status and behavior. Int J Web Inf Syst 2(14):158–176

    Google Scholar 

  31. Fortunato S (2010) Community detection in graphs. Phys Rep 486:75–174

    MathSciNet  Google Scholar 

  32. Gao F, Musial K, Gabrys B (2017) A community bridge boosting social network link prediction model. In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp 683–689. https://doi.org/10.1145/3110025.3110143

  33. Gao M, Chen L, Li B, Liu W (2018) A link prediction algorithm based on low-rank matrix completion. Appl Intell 48(12):4531–4550. https://doi.org/10.1007/s10489-018-1220-4

    Article  Google Scholar 

  34. Ghafari SM, Beheshti A, Yakhchi S, Orgun M (2018) Social contextaware trust prediction: A method for identifying fake news, The 19TH WISE, p 161

  35. Ghafari SM, Yakhchi S, Beheshti A, Orgun M (2018) SETTRUST: social exchange theory based context-aware trust prediction in online social networks, Data Quality and Trust in Big Data - 5th International Workshop, p 46

  36. Ghavipour M, Meybodi M (2018) Trust propagation algorithm based on learning automata for inferring local trust in online social networks. Knowl Based Syst 143:307

  37. Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proc Natl Acad Sci 99(12):7821

    MathSciNet  MATH  Google Scholar 

  38. Golbeck J, Parsia B, Hendler JA (2003) Trust Networks on the Semantic Web. In: Klusch M, Omicini A, Ossowski S, Laamanen H (eds) Cooperative Information Agents VII. CIA 2003. Lecture Notes in Computer Science, vol 2782. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45217-1_18

  39. Golzardi E, Fatemi A (2019) a novel methodology for clustering using the Kullback-Leibler index, 1st race of the international conference on engineering sciences in Iran

  40. Golzardi E, Sheikhahmadi A, Abdollahpouri A (2019) Detection of trust links on social networks using dynamic features, Physica A, vol. 527

  41. Goyal P, Ferrara E (2018) Graph embedding techniques, applications, and performance: a survey. Knowl-Based Syst 151:78–94

  42. Grover A, Leskovec J (2016) Node2vec: scalable feature learning for networks, arXiv:1607.00653

  43. Guimerà R, Sales-Pardo M (2009) Missing and spurious interactions and the reconstruction of complex networks. Proceed National Acad Sci 106(52):22

  44. Guo G, Zhang J, Zhu F, Wang X (2017) Factored similarity models with social trust for top-n item recommendation. Knowl-Based Syst 122:17–25

    Google Scholar 

  45. Huang Z (2010) Link prediction based on graph topology: the predictive value of generalized clustering coefficient, Electronic copy available at: https://ssrn.com/abstract=1634014. Accessed 22 Mar 2017

  46. Huang X, Chen D, Ren T, Wang D (2021) A survey of community detection methods in multilayer networks. Data Min Knowl 35:1–45. https://doi.org/10.1007/s10618-020-00716-6

    Article  MathSciNet  MATH  Google Scholar 

  47. Jeh G, Widom J (2002) SimRank, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, p 538

  48. Jeon H, Kim T (2017) Community-adaptive link prediction, Proc Int Conf Data Mining, p 1

  49. Jiang L, Cheng Y, Yang L, Li J, Yan H, Wa X (2023, 2019) A trustbased collaborative filtering algorithm for e-commerce recommendation system. J Ambient Intell Humaniz Comput 10(8)

  50. Junuthula RR, Xu KS, Devabhaktuni VK (2018) Leveraging friendship networks for dynamic link prediction in social interaction networks, Proc. 12th Int. AAAI Conf. Web Social Media, p 1

  51. Katz L (1953) A new status index derived from sociometric analysis. Psychometrika 18(1):39

    MATH  Google Scholar 

  52. Kernighan BW, Lin S (1970) An efficient heuristic procedure for partitioning graphs. Bell Syst Tech J 49(2):291

    MATH  Google Scholar 

  53. Leicht EA, Holme P, Newman ME (2006) Vertex similarity in networks. Phys Rev E 73(2):26120

  54. Li X, Li X (2017) Reconstruction of stochastic temporal networks through diffusive arrival times. Nat Commun 8:1

  55. Li FH, He J, Huang GY, Zhang YC, Shi Y (2014) A clustering-based link prediction method in social networks. Proc Int Conf Comput Sci 29:432–442

    Google Scholar 

  56. Li F, He J, Huang G, Zhang Y, Shi Y, Zhou R (2015) Node-coupling clustering approaches for link prediction. Knowl-Based Syst 89:669–680

    Google Scholar 

  57. Li D, Zhang Y, Xu Z, Chu D, Li S (2016) Exploiting information diffusion feature for link prediction in sina weibo, Sci Rep

  58. Li Z, Fang X, Sheng ORL (2017) A Survey of Link Recommendation for Social Networks. ACM Trans Manag Inf Syst 9(1):1

    Google Scholar 

  59. Liao H, Sebastian M, Medo M, Zhan YC (2017) Ranking in evolving complex networks. Phys Rep 689:1

    MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

  61. Liu Z, Zhang QM, Lü L, Zhou T (2011) Link prediction in complex networks: A local naïve Bayes model. EPL (Europhysics Letters) 96(4):48007. https://doi.org/10.1209/0295-5075/96/48007

    Article  Google Scholar 

  62. Liu G, Wang Y, Orgun M, Liu H (2012) Discovering trust networks for the selection of trustworthy service providers in complex contextual social networks, 2012 IEEE 19th International Conference on Web Services, p 384

  63. Liu S, Ji X, Liu C, Bai Y (2017) Extended resource allocation index for link prediction of complex network. Physica A 479:174–183

    MathSciNet  Google Scholar 

  64. Lü L, Zhou T (2011) Link prediction in complex networks: A survey. Phys A 390(6):1150

    Google Scholar 

  65. Lü L, Jin CH, Zhou T (2009) Similarity index based on local paths for link prediction of complex networks. Phys Rev E 80(4):046122

    Google Scholar 

  66. Lü L, Medo M, Yeung CH, Zhang YC, Zhang ZK, Zhou T (2012) Recommender systems. Phys Rep 519(1):1

  67. Ma C, Zhou T, Zhang HF (2016) Playing the role of weak clique property in link prediction: a friend recommendation model. Sci Rep 6:1. https://doi.org/10.1038/srep36143

    Article  Google Scholar 

  68. Ma C, Chen HS, Lai YC, Zhang HF (2018) Statistical inference approach to structural reconstruction of complex networks from binary time series. Phys Rev E, Stat Phys Plasmas Fluids Relat Interdiscip Top 97(2):022301. https://doi.org/10.1103/PhysRevE.97.022301

  69. MarInez I, Berzal F, Cubero JC (2016) A Survey of Link Prediction in Complex Networks. ACM Comput Surv 49(4):69

    Google Scholar 

  70. Menon AK, Elkan C (2011). Link prediction via matrix factorization. In: Gunopulos D, Hofmann T, Malerba D, Vazirgiannis M (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2011. Lecture Notes in Computer Science), vol 6912. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23783-6_28

  71. Naderan M, Namjoo E, Mohammadi S (2019) Trust classification in social networks using combined machine learning algorithms and fuzzy logic. Iran J Electric Electron Eng 3:294

    Google Scholar 

  72. Newman MEJ (2001) Clustering and preferential attachment in growing networks. Phys Rev E 64(2):25102

    Google Scholar 

  73. Pan L, Zhou T, Lü L, Hu CK (2016) Predicting missing links and identifying spurious links via likelihood analysis, Sci Rep, vol. 6, no. 22955

  74. Parvin H, Moradi P, Esmaeili S (2019) TCFACO: trust-aware collaborative filtering method based on ant colony optimization. Expert Syst Appl 118:152–168

  75. Pech R, Hao D, Pan L, Cheng H, Zhou T (2017) Link prediction via matrix completion. EPL (Europhysics Letters) 117(3):38002

  76. Pecli A, Cavalcanti MC, Goldschmidt R (2017) Automatic feature selection for supervised learning in link prediction applications: a comparative study, Knowl Inf Syst

  77. Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: online learning of social representations, arXiv: 1403.6652

  78. Rafiee S, Salavati C, Abdollahpouri A (2019) CNDP: link prediction based on common neighbors degree penalization, Physica A: Statistical Mechanics and its Applications  539:122950. https://doi.org/10.1016/j.physa.2019.122950

  79. Ravasz E (2002) Hierarchical organization of modularity in metabolic networks. Science 297(586):1551

  80. Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290:2323–2326

    Google Scholar 

  81. Ruan Y, Zhang P, Alfantoukh L, Durresi A (2017) Measurement theorybased trust management framework for online social communities. ACM Trans Internet Techn 17(2):1

  82. Sacco O, Breslin JG (2014) In users we trust: towards social user interactions based trust assertions for the social semantic web. Soc Netw Anal Min 4:1

    Google Scholar 

  83. Saeidi S (2020) A new model for calculating the maximum trust in Online Social Networks and solving by Artificial Bee Colony algorithm, Comput Soc Netw, vol. 7, no. 3

  84. Said A, Abbasi RA, Maqbool O, Daud A, Aljohani NR (2018) CCGA: a clustering coefficient based genetic algorithm for detecting communities in social networks. Appl Soft Comput J 63:59–70

  85. Sheikhahmadi A, Veisi F, Sheikhahmadi A, Mohammadimajd S (2022) A multi-attribute method for ranking influential nodes in complex networks. Plos One 11(17):e0278129

    Google Scholar 

  86. Sherchan W, Nepal S, Paris C (2013) A survey of trust in social networks. ACM Comput Surv 45:1–33

    Google Scholar 

  87. Soundarajan S, Hopcroft J (2012) Using community information to improve the precision of link prediction methods. Proc Int Conf World Wide Web, Lyon, p 607. https://doi.org/10.1145/2187980.2188150

  88. Sprinzak E, Sattath S, Margalit H (2003) How Reliable are Experimental Protein–Protein Interaction Data. J Mol Biol 327(5):919

    Google Scholar 

  89. Stumpf MPH, Thorne T, de Silva E, Stewar R, An HJ, Lappe M, Wiuf C (2008) Estimating the size of the human interactome. Proceed National Acad Sci 105(19):6959

  90. Tan F, Xia Y, Zhu B (2014) Link Prediction in Complex Networks: A Mutual Information Perspective. Plos One 9(9):e107056

    Google Scholar 

  91. Tang T, Jiang S, Chen X, Wang H, Wang W, Wang W (2020) Interlayer link prediction in multiplex social networks: An iterative degree penalty algorithm, Knowl Based Syst, vol. 194

  92. Trifunovic S, Legendre F, Anastasiades C (2010) Social trust in opportunistic networks. In: INFOCOM IEEE Conference on Computer Communications Workshops, San Diego, pp 1–6. https://doi.org/10.1109/INFCOMW.2010.5466696

  93. Valverde-Rebaza J, Lopes AD (2012) Structural link prediction using community information on Twitter, Proc 4th Int Conf Comput Aspects Social Netw (Cason), p. 132

  94. Valverde-rebaza J, Lopes AD (2012) Link prediction in complex networks based on cluster information. Adv Artif Intell 4:92

    Google Scholar 

  95. Wang P, Xu B, Wu Y, Zhou X (2015) Link prediction in social networks: the state-of-the-art. Sci China Inf Sci 58(1):1

    Google Scholar 

  96. Wang Y, Li L, Liu G (2015) Social context-aware trust inference for trust enhancement in social network based recommendations on service providers, World Wide Web, vol. 18

  97. Wang J, Ma Y, Liu M, Yuan H, Shen W, Li L (2017) A vertex similarity index using community information to improve link prediction accuracy. In: IEEE Int Conf Syst, Man, Cybern, Banff, pp 158-163. https://doi.org/10.1109/SMC.2017.8122595

  98. Wu Z, Lin Y, Wang J, Gregory S (2016) Link prediction with node clustering coefficient. Physica A 452:1–8

  99. Yan B, Gregory S (2012) Finding missing edges in networks based on their community structure. Phys Rev E 85(5):056112. https://doi.org/10.1103/PhysRevE.85.056112

    Article  Google Scholar 

  100. Yang J, Zhang XD (2016) Predicting missing links in complex networks based on common neighbors and distance, Sci Rep, vol. 6, no. 38208

  101. Yang L, Wang X, Luo MM (2021) Trust and Closeness: A Mixed Method for Understanding the Relationship of Social Network Users, J Int Technol Inf Manag, vol. 30, no. 1

  102. Yu Q, Long C, Lv Y, Shao H, He P, Duan Z (2014) Predicting Co-Author Relationship in Medical Co-Authorship Networks. Plos One 9(7):1

    Google Scholar 

  103. Yu Z, Wang C, Bu J, Wang X, Wu Y, Chen C (2015) Friend recommendation with content spread enhancement in social networks. Inf Sci 309:102–118

    Google Scholar 

  104. Zhang J (2017) Uncovering mechanisms of co-authorship evolution by multirelations-based link prediction. Inf Process Manag 53(1):42

    Google Scholar 

  105. Zhang Y, Yu T (2012) Mining trust relationships from online social networks. J Comput Sci Technol 27(3):492

    Google Scholar 

  106. Zhang QM, Lü L, Wang WQ, Zhu YX, Zhou T (2013) Potential Theory for Directed Networks. Plos One 8(2):55437

    Google Scholar 

  107. Zhang QM, Xu XK, Zhu YX, Zhou T (2015) Measuring multiple evolution mechanisms of complex networks. Sci Rep 5:10350

    Google Scholar 

  108. Zhang B, Huan Z, Li M, Qin Z, Huang J (2017) Trust traversal: a trust link detection scheme in social network. Comput Netw 120:105–125

  109. Zhang X, Pang W, Xia Y (2018) An intermediary probability model for link prediction. Physica A 512:902–912

  110. Zhang HF, Xu F, Bao ZK, Ma C (2019) Reconstructing of networks with binary-state dynamics via generalized statistical inference. IEEE Trans Circuits Syst I Reg Papers 66(4):1608–1619. https://doi.org/10.1109/TCSI.2018.2886770

    Article  MathSciNet  MATH  Google Scholar 

  111. Zhou T, Lü L, Zhang YC (2009) Predicting missing links via local information. Eur Phys J B 71(4):623–630. https://doi.org/10.1140/epjb/e2009-00335-8

    Article  MATH  Google Scholar 

  112. Zhu B, Xia Y (2015) An information-theoretic model for link prediction in complex networks. Sci Rep 5(1):13707

    MathSciNet  Google Scholar 

  113. Zitnik M, Agrawal M, Leskovec J (2018) Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics 34:i457–i466

Download references

Funding

The authors does not have financial and non-financial conflict of interest.

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Islamic Azad University, Sanandaj Branch, Sanandaj, Iran

    Elaheh Golzardi & Amir Sheikhahmadi

  2. Department of Computer Engineering, University of Kurdistan, Sanandaj, Iran

    Alireza Abdollahpouri

Authors
  1. Elaheh Golzardi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amir Sheikhahmadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alireza Abdollahpouri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amir Sheikhahmadi.

Ethics declarations

Conflict of interests

Elaheh Golzardi, Amir Sheikhahmadi and Alireza Abdollahpouri declare that they have no conflict of interest.

Additional information

Open access

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third-party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Golzardi, E., Sheikhahmadi, A. & Abdollahpouri, A. TRTCD: trust route prediction based on trusted community detection. Multimed Tools Appl 82, 41571–41607 (2023). https://doi.org/10.1007/s11042-023-15096-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-15096-4

Keywords

Search

Navigation