BiNet: trust sub-network extraction using binary ant colony algorithm in contextual social networks

Xiaoming Zheng, Yan Wang, Mehmet A. Orgun

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

7 Citations (Scopus)


Online Social Networks (OSNs) have become an integral part of daily life in recent years. OSNs contain important participants, the trust relations between participants, and the contexts in which participants interact with each other. All of these have a great influence on the prediction of the trust between a source participant and a target participant, which is important for a participant's decision-making process in many applications, such as seeking service providers. However, predicting the trust from a source participant to a target one based on the whole social network is not really feasible. Thus, prior to trust prediction, the extraction of a small-scale sub-network containing most of the important nodes and contextual information with a high density rate could make 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 BiNet: a social context-aware trust sub-network extraction model to search for near-optimal solutions effectively and efficiently. In this model, we first capture important factors that affect the trust between participants in OSNs. Next, we define a utility function to measure the trust factors of each node in a social network. At last, we design a novel binary ant colony algorithm with newly designed initialization and mutation processes for sub-network extraction incorporating the utility function. The experiments, conducted on two popular datasets of Epinion and Slash dot, demonstrate that our approach can extract sub-networks covering important participants and contextual information while keeping a high density rate. Our approach is superior to the state-of-the-art approaches in terms of the quality of extracted sub-networks within the same execution time.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE International Conference on Web Services, ICWS 2015
EditorsJohn A. Miller, Hong Zhu
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Electronic)9781467372725, 9781467372718
ISBN (Print)9781467372732
Publication statusPublished - 2015
EventIEEE International Conference on Web Services, ICWS 2015 - New York, United States
Duration: 27 Jun 20152 Jul 2015


OtherIEEE International Conference on Web Services, ICWS 2015
CountryUnited States
CityNew York


  • Trust
  • Sub-network extraction
  • Trust prediction


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