Using probabilistic confidence models for trust inference in Web-based social networks

Kuter, Ugur; Golbeck, Jennifer

doi:10.1145/1754393.1754397

Cited by 94 publications

(75 citation statements)

References 4 publications

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“…In this work, we focus on uncertain graphs, where our knowledge is represented as a graph, and there is uncertainty in the presence of each edge in the graph. Uncertain graphs have been used extensively in modeling, for example, in communication networks [1], [2], [3], social networks [4], [5], [6], [7], [8], [9], protein interaction networks [10], [11], [12], and regulatory networks in biological systems [13].…”

Section: Enumeration Of Maximal Cliques From An Uncertain Graphmentioning

confidence: 99%

Enumeration of Maximal Cliques from an Uncertain Graph

Mukherjee

Tirthapura

2017

IEEE Trans. Knowl. Data Eng.

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We consider the enumeration of dense substructures (maximal cliques) from an uncertain graph. For parameter 0 ;a ;1, we define the notion of an a-maximal clique in an uncertain graph. We present matching upper and lower bounds on the number of a-maximal cliques possible within a (uncertain) graph. We present an algorithm to enumerate a-maximal cliques whose worst-case runtime is near-optimal, and an experimental evaluation showing the practical utility of the algorithm. KeywordsRuntime, Proteins, Algorithm design and analysis, Social network services, Uncertainty, Bioinformatics Disciplines Electrical and Computer EngineeringComments This is a manuscript of an article published as Mukherjee, Arko Provo, Pan Xu, and Srikanta Tirthapura. Abstract-We consider the enumeration of dense substructures (maximal cliques) from an uncertain graph. For parameter 0 < a < 1, we define the notion of an a-maximal clique in an uncertain graph. We present matching upper and lower bounds on the number of a-maximal cliques possible within a (uncertain) graph. We present an algorithm to enumerate a-maximal cliques whose worst-case runtime is near-optimal, and an experimental evaluation showing the practical utility of the algorithm.

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Section: Enumeration Of Maximal Cliques From An Uncertain Graphmentioning

confidence: 99%

Enumeration of Maximal Cliques from an Uncertain Graph

Mukherjee

Tirthapura

2017

IEEE Trans. Knowl. Data Eng.

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“…Most of the existing works, even those in recent years [Kuter, et al 2010, Guha, et al 2004, Ortega, et al 2012, Su et al 2013, are based on the uniform trust propagation model for addressing the rating sparsity problems and malicious collective problems in trust and reputation management. However, we argue that trust and reputation in real world never follow the uniform propagation model.…”

Section: Conditional Trust Propagation Based On Rating Similarity Thrmentioning

confidence: 99%

Reliable and Resilient Trust Management in Distributed Service Provision Networks

Liu

et al. 2015

ACM Trans. Web

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Distributed service networks are popular platforms for service providers to offer services to consumers and for service consumers to acquire services from unknown parties. eBay and Amazon are two well-known examples of enabling and hosting such service networks to connect service providers to service consumers. Trust management is a critical component for scaling such distributed service networks to a large and growing number of participants. In this paper, we present ServiceTrust ++ , a feedback quality sensitive and attack resilient trust management scheme for empowering distributed service networks with effective trust management capability. Comparing with existing trust models, ServiceTrust ++ has several novel features. First, we present six attack models to capture both independent and colluding attacks with malicious cliques, malicious spies and malicious camouflages. Second, we aggregate the feedback ratings based on the variances of participants' feedback behaviors and incorporate feedback similarity as weight into the local trust algorithm. Third, we compute the global trust of a participant by employing conditional trust propagation based on feedback similarity threshold. This allows ServiceTrust ++ to control and prevent malicious spies and malicious camouflage peers to boost their global trust scores by manipulating the feedback ratings of good peers and by taking advantage of the uniform trust propagation. Finally, we systematically combine trust decaying strategy with threshold-value based conditional trust propagation to further strengthen the robustness of our global trust computation against sophisticated malicious feedbacks. Experimental evaluation with both simulation-based networks and real network dataset Epinion show that ServiceTrust ++ is highly resilient against all six attack models and highly effective compared to EigenTrust, the most popular and representative trust propagation model to date.

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“…Such algorithms include Advogato [6], Appleseed [7], Sunny [8], and Moletrust [9]. These algorithms use trust that is assigned on a fixed scale (e.g.…”

Section: Related Workmentioning

confidence: 99%

Predicting Trust and Distrust in Social Networks

DuBois

Golbeck

Srinivasan

2011

2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing

Self Cite

117

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Abstract-As user-generated content and interactions have overtaken the web as the default mode of use, questions of whom and what to trust have become increasingly important. Fortunately, online social networks and social media have made it easy for users to indicate whom they trust and whom they do not. However, this does not solve the problem since each user is only likely to know a tiny fraction of other users; we must have methods for inferring trust -and distrust -between users who do not know one another. In this paper, we present a new method for computing both trust and distrust (i.e., positive and negative trust). We do this by combining an inference algorithm that relies on a probabilistic interpretation of trust based on random graphs with a modified spring-embedding algorithm. Our algorithm correctly classifies hidden trust edges as positive or negative with high accuracy. These results are useful in a wide range of social web applications where trust is important to user behavior and satisfaction.

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Using probabilistic confidence models for trust inference in Web-based social networks

Cited by 94 publications

References 4 publications

Enumeration of Maximal Cliques from an Uncertain Graph

Enumeration of Maximal Cliques from an Uncertain Graph

Reliable and Resilient Trust Management in Distributed Service Provision Networks

Predicting Trust and Distrust in Social Networks

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