Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.14/192097
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- Title
- Discovering trust networks for the selection of trustworthy service providers in complex contextual social networks
- Related
- International Conference on Web Services (19th : 2012) (24 - 29 June 2012 : Honolulu, Hawaii, USA)
- Related
- Goble, Carole; Chen, Peter and Zhang, Jia. ICWS 2012 : IEEE 19th International Conference on Web Services : proceedings : 24-29 June 2012, Honolulu, Hawaii, USA, p.384-391
- DOI
- 10.1109/ICWS.2012.47
- Publisher
- Piscataway, NJ : IEEE
- Date
- 2012
- Author/Creator
- Liu, Guanfeng
- Author/Creator
- Wang, Yan
- Author/Creator
- Orgun, Mehmet A
- Author/Creator
- Liu, Huan
- Description
- Online Social Networks (OSNs) have provided an infrastructure for a number of emerging applications in recent years, e.g., for the recommendation of service providers, where trust is one of the most important factors for the decision-making of service consumers. In order to evaluate the trustworthiness of a service provider (i.e., the target) without any prior interaction with a service consumer (i.e., the source), the trust network from the source to the target need to be extracted firstly before performing any trust evaluation, as it contains some important intermediate participants, the trust relations between the participants, and the social context, each of which has an important influence on trust evaluation. However, the network extraction has been proved to be NP-Complete. Towards solving this challenging problem, we first propose a complex contextual social network structure which considers some social contexts, having significant influences on both social interactions and trust evaluation between participants. Then, we propose a new concept called QoTN (Quality of Trust Network) and a social context-aware trust network discovery model. Finally, we propose a Heuristic Social Context-Aware trust Network discovery algorithm (H-SCAN) by adopting the K-Best-First Search (KBFS) method and our optimization strategies. The experimental results illustrate that our proposed model and algorithm outperform the existing methods in both algorithm efficiency and the quality of the extracted trust networks.
- Description
- 8 page(s)
- Subject Keyword
- service provider selection
- Subject Keyword
- social networks
- Subject Keyword
- trust
- Resource Type
- conference paper
- Organisation
- Macquarie University. Dept. of Computing
- Identifier
- http://hdl.handle.net/1959.14/192097
- Identifier
- ISBN:9781467321310
- Identifier
- mq_res-ext-ieee20120928343-10
- Language
- eng
- Reviewed
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