Mehmet Ali Orgun

Publications (172) View all

• Source

  Available from: Yan Wang

  Conference Proceeding: Social Context-Aware Trust Network Discovery in Complex Contextual Social Networks

  Guanfeng Liu, Yan Wang, Mehmet A Orgun
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  ABSTRACT: Trust is one of the most important factors for par-ticipants' decision-making in Online Social Networks (OSNs). The trust network from a source to a target without any prior interaction 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. Thus, before performing any trust evaluation, the contextual trust network from a given source to a target needs to be extracted first, where constraints on the social con-text should also be considered to guarantee the quality of extracted networks. However, this problem has been proved to be NP-Complete. Towards solving this chal-lenging problem, we first propose a complex contextual social network structure which considers social contex-tual impact factors. These factors have significant in-fluences on both social interaction between participants and trust evaluation. 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 Social Context-Aware trust Network dis-covery algorithm (SCAN) by adopting the Monte Carlo method and our proposed 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 network.
  Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12); 08/2012
• Source

  Available from: Yan Wang

  Conference Proceeding: Discovering Trust Networks for the Selection of Trustworthy Service Providers in Complex Contextual Social Networks

  Guanfeng Liu, Yan Wang, Mehmet A Orgun, Huan Liu
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  ABSTRACT: 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 impor-tant 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 pro-pose 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 effi-ciency and the quality of the extracted trust networks.
  IEEE 9th International Conference on Web Services (IEEE ICWS 2012); 06/2012
• Conference Proceeding: 5th International Conference of Security of Information and Networks, SIN '12, Jaipur, India, October 22 - 26, 2012

  Manoj Singh Gaur, Atilla Elçi, Oleg B Makarevich, Mehmet A Orgun, Virendra Singh
  SIN; 01/2012
• Article: Finding the Optimal Social Trust Path for the Selection of Trustworthy Service Providers in Complex Social Networks

  Guanfeng Liu, Mehmet A Orgun, Ee-Peng Lim
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  ABSTRACT: Online Social networks have provided the infrastructure for a number of emerging applications in recent years, e.g., for the recommendation of service providers or the recommendation of files as services. In these applications, trust is one of the most important factors in decision making by a service consumer, requiring the evaluation of the trustworthiness of a service provider along the social trust paths from a service consumer to the service provider. However, there are usually many social trust paths between two participants who are unknown to one another. In addition, some social information, such as social relationships between participants and the recommendation roles of participants, has significant influence on trust evaluation but has been neglected in existing studies of online social networks. Furthermore, it is a challenging problem to search the optimal social trust path that can yield the most trustworthy evaluation result and satisfy a service consumer's trust evaluation criteria based on social information. In this paper, we first present a novel complex social network structure incorporating trust, social relationships and recommen-dation roles, and introduce a new concept, Quality of Trust (QoT), containing the above social information as attributes. We then model the optimal social trust path selection problem with multiple end-to-end QoT constraints as a Multi-Constrained Optimal Path (MCOP) selection problem, which is shown to be NP-Complete. To deal with this challenging problem, we propose a novel Multiple Foreseen Path-Based Heuristic algorithm MFPB-HOSTP for the Optimal Social Trust Path selection, where multiple backward local social trust paths (BLPs) are identified and concatenated with one Forward Local Path (FLP), forming multiple foreseen paths. Our strategy not only could help avoid failed feasibility estimation in path selection in certain cases, but also increase the chances of delivering a near-optimal solution with high quality. The results of our experiments conducted on a real dataset of online social networks illustrate that MFPB-HOSTP algorithm can efficiently identify the social trust paths with better quality than our previously proposed H OSTP algorithm that outperforms prior algorithms for the MCOP selection problem.
  IEEE Transactions on Services Computing 01/2011; 1. · 1.47 Impact Factor
• Article: A Tabu-Harmony Search-Based Approach to Fuzzy Linear Regression.

  M. Hadi Mashinchi, Mehmet A. Orgun, M. Mashinchi, Witold Pedrycz
  IEEE T. Fuzzy Systems. 01/2011; 19:432-448.