Strong social component-aware trust sub-network extraction in contextual social networks

Guanfeng Liu, Yan Wang, Mehmet A. Orgun, Xiaoming Zheng, An Liu, Zhixu Li, Kai Zheng

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

3 Citations (Scopus)

Abstract

In Online Social Networks (OSNs), the important participants, the trust relations between participants, and the interaction contexts between participants greatly impact a participant's decision-making in many applications, such as service provider selection and crowdsourcing service invocation. However, predicting the trust between two unknown participants based on the whole large-scale social network can lead to very high computation costs. Thus, prior to trust prediction, extracting a small-scale sub-network containing the important participants and the corresponding contextual information with a high density could make the trust prediction more efficient and effective. However, extracting such a sub-network has been proved to be an NP-Complete problem. To address this challenging problem, we propose a strong social component-aware trust sub-network extraction model, So-BiNet, to search for near-optimal solutions effectively and efficiently. Our method can extract a trust sub-network without any decompression, which can in turn greatly save the search time of trust sub-network extraction. The experiments, conducted on four social network datasets, demonstrate that our approach can efficiently extract sub-networks covering important participants and contextual information while keeping a high density. Our approach is superior to the state-of-the-art approaches in terms of the quality of the sub-networks extracted within the same execution time.

Original languageEnglish
Title of host publicationICWS 2016
Subtitle of host publicationIEEE International Conference on Web Services : proceedings
EditorsStephan Reiff-Marganiec
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages107-114
Number of pages8
ISBN (Electronic)9781509026753
DOIs
Publication statusPublished - 31 Aug 2016
Event23rd IEEE International Conference on Web Services, ICWS 2016 - San Francisco, United States
Duration: 27 Jun 20162 Jul 2016

Other

Other23rd IEEE International Conference on Web Services, ICWS 2016
CountryUnited States
CitySan Francisco
Period27/06/162/07/16

Keywords

  • sub-network extraction
  • trust
  • trust prediction

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