A Novel Optimal Social Trust Path Selection Algorithm for Large-Scale Complex Social Networks

Liu, Lianggui; Jia, Huiling

doi:10.1587/transcom.e97.b.1910

Cited by 8 publications

(11 citation statements)

References 17 publications

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“…In the work done by Liu et al 12,13,14 the concept of Quality of Trust Transitivity (QoTT) has been proposed. They have included the impact of social relationship, recommendation roles and preference similarity into account to compute transitive trust but they haven't discussed the limit on the number of hops from source to sink while estimating indirect trust from source to sink.…”

Section: Related Studymentioning

confidence: 99%

Controlled Expansion of Neighborhood in Trust Based Recommender Systems

Jain

Kaur

2015

Procedia Computer Science

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A technique to identify good recommenders and make them trustworthy neighbours is presented. By having good recommenders in direct association, an agent will have an improved set of recommendations in lesser amount of time and computations as compared to getting recommendations transitively from neighbour of a neighbour and so on. While estimating trust on an unknown agent, the upper limit on the number of hops one needs to explore to reach that agent is also proposed, hence further minimizing time and computation complexity. Results of experiments conducted on a real dataset illustrate the efficiency and effectiveness of the proposed method.

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Section: Related Studymentioning

confidence: 99%

Controlled Expansion of Neighborhood in Trust Based Recommender Systems

Jain

Kaur

2015

Procedia Computer Science

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“…A basic and frequently used method called Iterative Multiplication Strategy (IMS), calculates trust by multiplying trust values of all participants along the social trust path [23,24,25,26,27,28]. If there are n nodes in a path from (source) to (target), the aggregated trust value holds for will be…”

Section: Related Workmentioning

confidence: 99%

“…Some other researchers such as Liu et al in [23], consider other measures that leads to a novel approach called Quality of Trust (QoT) which selects the best path according to some constraints.…”

Section: Related Workmentioning

confidence: 99%

Alleviating controversiality problem in trust estimation

Mohkami

Shakeri

2012

6th International Symposium on Telecommunications (IST)

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Trustworthiness of users has become an indispensable aspect of security in networks. Not having an experience of interacting among nodes is a challenge for this phenomenon. Trust Management(TM) systems brought into play to bring security to any kind of network. Many web-based social networks assign users unique scores based on their interaction with their neighbors. Others, however, argue that trustworthiness of a user varies according to the different views of different users, and consider the so-called local trust metrics. Due to the existence of controversial users, users who are trusted and distrusted by many, in any social community, we have to consider other facts to estimate trusts with better accuracy. There is a strong bond between sociology and trust. Therefore, in this paper, we propose PredSimTrust, a trust estimation algorithm which estimates trust of a target user based on the similarities and trusts of predecessors. We examined the proposed algorithm on a real user community. The results show that our algorithm outperforms other models such as Iterative Multiplication strategy (IMS) and other models which take just one of these notions (trust value and similarity) into consideration.

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“…According to the model in [7], trust in a social network includes three factors: trust, social intimacy degree, and role impact factor. Ref.…”

Section: Model Of Complex Service-oriented Social Networkmentioning

confidence: 99%

“…Liu et al [7] propose a classical social network model to resolve the optimal social trust path selection problem, which takes trust information, social relationships, and social role of each participant into consideration. In their work, finding the optimal social trust path is transformed into the Multi-Constrained Optimal Path (MCOP) selection problem, which is NP-complete.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Search Strategy for Service Provider Selection in Complex Social Networks

Liu

Tang

et al. 2012

2012 IEEE Ninth International Conference on Services Computing

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The trustworthiness of service providers plays an important role when a consumer selects a service. This paper studies the problem of how to efficiently search and select trustworthy service providers for users in social networks consisting of service providers and consumers. A trust value between two participants can be derived by existing methods from the optimal trust path between them in a social network. When more than one trust factors are taken into consideration, the exact optimal trust path selection algorithm is NP-complete. Although several heuristic algorithms have been proposed to find approximate solutions, their time complexities are still too high to be acceptable in practice, especially when they are used in very large scale social networks. Focusing on reducing trust path searching time, this paper proposes an efficient preprocessing-based search strategy. It exploits structural properties of the social networks and builds an advanced data structure from preprocessing, which can be used to simplify and accelerate the trust path searching. Experimental results show our strategy is very efficient and nearly achieves a constant time complexity. The computed trustworthiness based on our method has excellent performance close to that of the best existing heuristic algorithm.

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A Novel Optimal Social Trust Path Selection Algorithm for Large-Scale Complex Social Networks

Cited by 8 publications

References 17 publications

Controlled Expansion of Neighborhood in Trust Based Recommender Systems

Controlled Expansion of Neighborhood in Trust Based Recommender Systems

Alleviating controversiality problem in trust estimation

An Efficient Search Strategy for Service Provider Selection in Complex Social Networks

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