World Wide Web

, Volume 21, Issue 3, pp 713–738 | Cite as

Context-aware trust network extraction in large-scale trust-oriented social networks

  • Guanfeng Liu
  • Yi Liu
  • An Liu
  • Zhixu Li
  • Kai Zheng
  • Yan Wang
  • Xiaofang Zhou


In recent years, social networking sites have been used as a means for a rich variety of activities, such as movie recommendations and product recommendations. In order to evaluate the trust between a truster (i.e., the source) and a trustee (i.e., the target) who have no direct interaction in Online Social Networks (OSNs), the trust network between them that contains important intermediate participants, the trust relations between the participants, and the social context, has an important influence on trust evaluation. Thus, to deliver a reasonable trust evaluation result, before performing any trust evaluation (i.e., trust transitivity), the contextual trust network from a given source to a given target needs to be first extracted from the social network, where constraints on social context should also be considered to guarantee the quality of the extracted networks. However, this problem has been proved to be NP-Complete. Towards solving this challenging problem, we first present a contextual trust-oriented social network structure which takes social contextual impact factors, including trust, social intimacy degree, community impact factor, preference similarity and residential location distance into account. These factors have significant influences on both social interactions between participants and trust evaluation. Then, we present a new concept QoTN (Quality of Trust Network) and propose a social context-aware trust network extraction model. Finally, we propose a Heuristic Social Context-Aware trust Network extraction algorithm (H-SCAN-K) by extending the K-Best-First Search (KBFS) method with several proposed optimization strategies. The experiments conducted on two real datasets illustrate that our proposed model and algorithm outperform the existing methods in both algorithm efficiency and the quality of the extracted trust networks.


Trust Subnetwork Social networks 



This work was partially supported by Natural Science Foundation of China (Grant Nos. 61303019, 61572336, 61532018, 61402313, 61502324), Doctoral Fund of Ministry of Education of China (20133201120012), Postdoctoral Science Foundation of China (2015M571805, 2016T90492), Collaborative Innovation Center of Novel Software Technology and Industrialization, Jiangsu, China, and the Opening Project of Guangdong Province Key Laboratory of Big Data Analysis and Processing (2017002).


  1. 1.
    Adamic, L.A., Lukose, R.M., Huberman, B.A.: Local search in unstructured networks. In: Handbook of Graphs and Networks. Wiley (2005)Google Scholar
  2. 2.
    Adler, P.S.: Market, hierarchy, and trust: the knowledge economy and the future of capitalism. Organ. Sci. 12(12), 215–234 (2001)CrossRefGoogle Scholar
  3. 3.
    Baase, S., Gelder, A.: Computer Algorithms Introduction to Design and Analysis. Addison Wesley (1999)Google Scholar
  4. 4.
    Barnett, E., Casper, M.: A definition of social environment. Am. J. Public Health 91(3), 465 (2001)Google Scholar
  5. 5.
    Brass, D.J.: A Socal Network Prespective on Industral/Organizational Psychology. Industrial/ Organizational Handbook (2009)Google Scholar
  6. 6.
    Chang, N., Liu, M.: Revisiting the ttl-based controlled flooding search: Optimality and randomization. In: MobiCom, pp. 85–99 (2004)Google Scholar
  7. 7.
    Chia, P.H., Pitsilis, G.: Exploring the use of explicit trust link for filtering recommenders: a study on J. Inf. Process. 19, 332–344 (2011)Google Scholar
  8. 8.
    Choi, M.H.K., Croft, W.: Dependency trigram model for social relation extraction from news articles. In: SIGIR, pp. 1047–1048 (2012)Google Scholar
  9. 9.
    Christianson, B., Harbison, W.S.: Why isn’t trust transitivie?. In: International Workshop on Security Protocols, pp. 171–176 (1996)Google Scholar
  10. 10.
    Chua, F., Lim, E.P.: Trust network inference for online rating data using generative models. In: KDD, pp. 889–898 (2010)Google Scholar
  11. 11.
    Dalton, M.: Men Who Manage. Wiley, New York (1959)Google Scholar
  12. 12.
    Felner, A., Kraus, S., Korf, R.E.: Kbfs: K-best-first search. Ann. Math. Artif. Intell. 39, 19–39 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Filali, I., Huet, F.: Dynamic ttl-based search in unstructured peer-to-peer networks. In: CCGrid’10, pp. 438–447 (2010)Google Scholar
  14. 14.
    Freeh, V., Lowenthal, D.K., Pan, F., Kappiah, N., Springer, R., Rountree, B.L., Femal, M.E.: Analyzing the energy-time trade-off in high-performance computing applications. IEEE Trans. Parallel Distrib. Syst. 18(6), 835–848 (2007)CrossRefGoogle Scholar
  15. 15.
    Gimpel, J., Karnes, K., Mctague, J., Pearson-Merkowitz, S.: Distance-decay in the political geography of friends-and-neighbors voting. Polit. Geogr. 27, 231–252 (2008)CrossRefGoogle Scholar
  16. 16.
    Gkantsidis, C., Mihail, M., Saberi, A.: Random walks in peer-to-peer networks. In: IEEE INFORCOM, pp. 120–130 (2004)Google Scholar
  17. 17.
    Golbeck, J., Hendler, J.: Inferring trust relationships in web-based social networks. ACM Trans. Internet Technol. 6(4), 497–529 (2006)CrossRefGoogle Scholar
  18. 18.
    Groh, G., Hauffa, J.: Characterizing social relations via nlp-based sentiment analysis. In: AAAI Conference on Weblogs and Social Media, pp. 502–505 (2011)Google Scholar
  19. 19.
    Iosup, A., Ostermann, S., Yigitbasi, N., Prodan, R., Fahringer, T., Epema, D.: Performance analysis of cloud computing services for many-tasks scientific computing. IEEE Trans. Parallel Distrib. Syst. 22(6), 931–945 (2011)CrossRefGoogle Scholar
  20. 20.
    Ira, P.: Bi-Directional Search. Edinburgh University Press, Edinburgh (1971)zbMATHGoogle Scholar
  21. 21.
    Korte, R.F.: Biases in decision making and implications for human resource development. Adv. Dev. Hum. Resour. 5(4), 440–457 (2003)CrossRefGoogle Scholar
  22. 22.
    Kuter, U., Golbeck, J.: Sunny: A new algorithm for trust inference in social networks using probabilistic confidence model. In: AAAI’07, pp. 1377–1382 (2007)Google Scholar
  23. 23.
    Lichtenstein, S., Slovic, P.: The Construction of Preference. Cambridge University Press (2006)Google Scholar
  24. 24.
    Liu, G., Wang, Y., Orgun, M., Lim, E.P.: A heuristic algorithm for trust-oriented service provider selection in complex social networks. In: SCC, pp. 130–137 (2010)Google Scholar
  25. 25.
    Liu, G., Wang, Y., Orgun, M.A.: Optimal social trust path selection in complex social networks. In: AAAI, pp. 1397–1398 (2010)Google Scholar
  26. 26.
    Liu, G., Wang, Y., Orgun, M.A.: Finding k optimal social trust paths for the selection of trustworthy service providers in complex social networks. In: ICWS, pp. 41–48 (2011)Google Scholar
  27. 27.
    Liu, G., Wang, Y., Orgun, M.A.: Trust transitivity in complex social networks. In: AAAI, pp. 1222–1229 (2011)Google Scholar
  28. 28.
    Liu, G., Wang, Y., Orgun, M.A.: Social Context-Aware Trust Network Discovery in Complex Contextual Social Networks. In: AAAI (2012)Google Scholar
  29. 29.
    Liu, G., Wang, Y., Orgun, M.A., Lim, E.P.: Finding the optimal social trust path for the selection of trustworthy service providers in complex social networks. IEEE Trans. Serv. Comput. doi: 10.1109/ICWS.2011.81 (2011)
  30. 30.
    Liu, G., Wang, Y., Orgun, M.A., Liu, H.: Discovering trust networks for the selection of trustworthy service providers in complex contextual social networks. In: ICWS, pp. 384–391 (2012)Google Scholar
  31. 31.
    Liu, G., Zheng, K., Wang, Y., Orgun, M.A., Liu, A., Zhao, L., Zhou, X.: Multi-constrained graph pattern matching in large-scale contextual social graphs. In: ICDE, pp. 351–362 (2015)Google Scholar
  32. 32.
    Liu, Q., Xiang, B., Yuan, N.J., Chen, E., Xiong, H., Zheng, Y., Yang, Y.: An influence propagation view of pagerank. ACM Trans. Knowl. Discov. Data 11 (3), 30:1–30:30 (2017)Google Scholar
  33. 33.
    Lo, D., Surian, D., Zhang, K., Lim, E.P.: Mining direct antagonistic communities in explicit trust networks. In: CIKM, pp. 1013–1018 (2011)Google Scholar
  34. 34.
    Luhmann, N.: Trust and Power. Wiley, Chichester (1979)Google Scholar
  35. 35.
    Mansell, R., Collins, B.: Trust and Crime in Information Societies. Edward Elgar Publishing, Cheltenham (2005)Google Scholar
  36. 36.
    Mccallum, A., Wang, X., Corrada-Emmanuel, A.: Topic and role discovery in social networks with experiments on Enron and academic email. J. Artif. Intell. Res. 30(1), 249–272 (2007)Google Scholar
  37. 37.
    Milgram, S.: The small world problem. Psychology Today 2(60), 61–67 (1967)Google Scholar
  38. 38.
    Miller, R., Perlman, D., Brehm, S.: Intimate Relationships, 4th edn. McGraw-Hill College, Boston (2007)Google Scholar
  39. 39.
    Mislove, A., Marcon, M., Gummadi, K., Druschel, P., Bhattacharjee, B.: Measurement and analysis of online social networks. In: ACM IMC, pp. 29–42 (2007)Google Scholar
  40. 40.
    Pool, I., Kochen, M.: Contacts and influence. Soc. Netw. 1, 1–48 (1978)MathSciNetCrossRefGoogle Scholar
  41. 41.
    Prell, C.L.: Community networking and social capital: early investigation. J. Comput.-Mediat. Commun. 8(3). doi: 10.1111/j.1083-6101.2003.tb00214.x (2003)
  42. 42.
    Sun, Y., Yu, W., Han, Z., Liu, K.: Information theoretic framework of trust modelling and evaluation for ad hoc networks. IEEE J. Sel. Areas Commun. 24 (2), 305–317 (2006)CrossRefGoogle Scholar
  43. 43.
    Tang, J.H., Gao, H.L., Sarma, A.D.: etrust: Understanding trust evolution in an online world. In: KDD, pp. 253–261 (2012)Google Scholar
  44. 44.
    Wang, G., Wu, J.: Multi-dimensional evidence-based trust management with multi-trusted paths. Futur. Gener. Comput. Syst. 27(5), 529–538 (2011)CrossRefGoogle Scholar
  45. 45.
    Wang, Y., Varadharajan, V.: Role-based recommendation and trust evaluation. In: IEEE EEE’07, pp. 278–295 (2007)Google Scholar
  46. 46.
    Wang, Y., Li, L., Liu, G.: Social context-aware trust inference for trust enhancement in social network based recommendations on service providers. World Wide Web Journal August 18(1), 159–184 (2013)CrossRefGoogle Scholar
  47. 47.
    Wren, J., Kozak, K., Johnson, K., Deakyne, S., Schilling, L., Dellavalle, R.: A survey of perceived contributions to papers based on byline position and number of authors. EMBO Rep. 8(11), 988–991 (2007)CrossRefGoogle Scholar
  48. 48.
    Yao, Y., Tong, H., Yan, X., Xu, F., Lu, J.: Matri: A multi-aspect and transitive trust inference model. In: WWW, pp. 1467–1476 (2013)Google Scholar
  49. 49.
    Yoo, S.Y., Yang, F.L., Moon, I.: Mining social networks for personalized email prioritization. In: KDD, pp. 967–976 (2009)Google Scholar
  50. 50.
    Zajonc, R.: Interpersonal attraction and attitude similarity. J. Abnorm. Soc. Psychol. 62(3), 713–715 (1961)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Guanfeng Liu
    • 1
    • 2
  • Yi Liu
    • 1
  • An Liu
    • 1
  • Zhixu Li
    • 1
  • Kai Zheng
    • 1
  • Yan Wang
    • 3
  • Xiaofang Zhou
    • 4
  1. 1.School of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.Guangdong Key Laboratory of Big Data Analysis and ProcessingGuangzhouPeople’s Republic of China
  3. 3.Department of ComputingMacquarie UniversitySydneyAustralia
  4. 4.School of Information Technology and Electrical EngineeringQueensland UniversityBrisbaneAustralia

Personalised recommendations