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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)713-738
Number of pages26
JournalWorld Wide Web
Issue number3
Publication statusPublished - May 2018


  • trust
  • subnetwork
  • social networks
  • Subnetwork
  • Social networks
  • Trust


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