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
Article

Abstract

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.

Keywords

Trust Subnetwork Social networks 

Notes

Acknowledgments

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).

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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

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