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Social Context-Aware Trust Network Discovery in Complex Contextual Social Networks
"... Trust is one of the most important factors for participants’ 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 con ..."
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Trust is one of the most important factors for participants’ 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 context should also be considered to guarantee the quality of extracted networks. However, this problem has been proved to be NP-Complete. Towards solving this challenging problem, we first propose a complex contextual social network structure which considers social contextual impact factors. These factors have significant influences 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 discovery 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.
Discovering Trust Networks for the Selection of Trustworthy Service Providers in Complex Contextual Social Networks
"... 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 tru ..."
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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 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.
2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications Multiple QoT Constrained Social Trust Path Selection in Complex Social Networks
"... Abstract—In recent years, online social networks with numerous participants have been used as the means for rich activities, where trust is one of the most important indications for participants ’ decision making, demanding the evaluation of the trustworthiness of a target participant along a certai ..."
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Abstract—In recent years, online social networks with numerous participants have been used as the means for rich activities, where trust is one of the most important indications for participants ’ decision making, demanding the evaluation of the trustworthiness of a target participant along a certain social trust path from a source participant. However, there are usually many social trust paths between participants. Thus, a challenging problem is how to select the optimal one from massive social trust paths yielding the most trustworthy trust evaluation result based on participants trust evaluation criteria. To address this issue, in this paper, we first propose a new Multiple QoT Constrained Social Trust Path (MQCSTP) selection model which considers both adjacent constraints and end-to-end constraints, based on a novel concept Quality of Trust (QoT) and a novel complex social network structure. We then model the MQCSTP selection as the classical NP-Complete Multi-Constrained Optimal Path (MCOP) selection problem. For solving this problem, we propose an effective and efficient heuristic algorithm, called H MQCSTP. The results of our experiments conducted on a real dataset of online social networks illustrate that the proposed method outperforms existing models in both efficiency and the quality of delivered solutions.
BiNet: Trust Sub-network Extraction using Binary Ant Colony Algorithm in Contextual Social Networks
"... Abstract—Online Social Networks (OSNs) have become an integral part of daily life in recent years. OSNs contain impor-tant 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 ..."
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Abstract—Online Social Networks (OSNs) have become an integral part of daily life in recent years. OSNs contain impor-tant 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 predic-tion 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 Slashdot, 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. Keywords-Trust; Sub-network extraction; Trust prediction; I.
COMMUNITY DETECTION IN SOCIAL NETWORKS: AN OVERVIEW
"... A social network can be defined as a set of people connected by a set of people. Social network analysis provides both a visual and a mathematical analysis of human relationship. The investigation of the community structure in the social network has been the important issue in many domains and disci ..."
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A social network can be defined as a set of people connected by a set of people. Social network analysis provides both a visual and a mathematical analysis of human relationship. The investigation of the community structure in the social network has been the important issue in many domains and disciplines. Community structure assumes more significance with the increasing popularity of online social network services like Facebook, MySpace, or Twitter. This paper reflects the emergence of communities that occur in the structure of social networks, represented as graphs. We have mainly discussed various community detection algorithms in real world networks in this paper. This paper represents as an overview of the community detection algorithms in social networks.
Chapter 1 Trust-Oriented Service Provider Selection in Complex Online Social Networks
"... Abstract In recent years, Online Social Networks (OSNs) with numerous partici-pants have been used as the means for rich activities. For example, employers could use OSNs to investigate potential employees, and participants could use OSNs to look for movie recommendations. In these activities, trust ..."
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Abstract In recent years, Online Social Networks (OSNs) with numerous partici-pants have been used as the means for rich activities. For example, employers could use OSNs to investigate potential employees, and participants could use OSNs to look for movie recommendations. In these activities, trust is one of the most impor-tant indication of participants decision making, greatly demanding the evaluation of the trustworthiness of a service provider along certain social trust paths from a service consumer. In this chapter, we first analyze the characteristics of the current generation of functional websites and the current generation of online social net-works based on their functionality and sociality, and present the properties of the new generation of social network based web applications. Then we present a new selection model considering both adjacent and end-to-end constraints, based on a novel concept Quality of Trust and a novel complex social network structure. More-over, in order to select the optimal one from massive social trust paths yielding the most trustworthy trust evaluation result, this chapter presents an effective and ef-ficient heuristic algorithm for optimal social trust path selection with constraints, which is actually an NP-Complete problem. Experimental results illustrate that the proposed method outperforms existing models in both efficiency and the quality of delivered solutions. This work provides key techniques to potentially lots of service-oriented applications with social networks as the backbone.
