Trust Inference in Web-Based Social Networks Using Resistive Networks

Taherian, Mohsen; Amini, Morteza; Jalili, Rasool

doi:10.1109/iciw.2008.41

Cited by 42 publications

(23 citation statements)

References 10 publications

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“…Other applications include personalized recommendations [17,16] and feed ranking in social networks [15]. Besides, patterns of influence can be taken as a sign of user trust and exploited for computing trust propagation [6,23,5,18] in large networks and in P2P systems.…”

Section: Introductionmentioning

confidence: 99%

Learning influence probabilities in social networks

Goyal

Bonchi

Lakshmanan

2010

Proceedings of the Third ACM International Conference on Web Search and Data Mining

901

773

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Recently, there has been tremendous interest in the phenomenon of influence propagation in social networks. The studies in this area assume they have as input to their problems a social graph with edges labeled with probabilities of influence between users. However, the question of where these probabilities come from or how they can be computed from real social network data has been largely ignored until now. Thus it is interesting to ask whether from a social graph and a log of actions by its users, one can build models of influence. This is the main problem attacked in this paper. In addition to proposing models and algorithms for learning the model parameters and for testing the learned models to make predictions, we also develop techniques for predicting the time by which a user may be expected to perform an action. We validate our ideas and techniques using the Flickr data set consisting of a social graph with 1.3M nodes, 40M edges, and an action log consisting of 35M tuples referring to 300K distinct actions. Beyond showing that there is genuine influence happening in a real social network, we show that our techniques have excellent prediction performance.

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Section: Introductionmentioning

confidence: 99%

Learning influence probabilities in social networks

Goyal

Bonchi

Lakshmanan

2010

Proceedings of the Third ACM International Conference on Web Search and Data Mining

901

773

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“…However the weakness of this method is ignoring useful information via eliminating some paths. (Taherian et al, 2008) was brought up in 2008. The main idea for this method is using Resistive Network (RN) concept to simulate trust networks.…”

Section: Current Trust Inference Mechanismsmentioning

confidence: 99%

Current Trust Inference Mechanisms in Web Based Social Networks

Harvey¹

2012

Journal of Computer Science

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Problem statement: Nowadays, web based social networks have gained a lot of attention. Over one billion users participate, communicate and share information on these networks based on trust. Approach: One important issue on online social networks is how much one person can trust another person in the network whom they are not directly connected. Scholars around the world have introduced a variety of models and algorithms for inferring trust in online social networks. This study takes a deep look at current methods, their advantages and disadvantages. Results: All current methods have weaknesses and cannot be considered the best solution for inferring trust. Conclusion: Considering the fact that web based social networks are based on trust upon participants, a more accurate and fast algorithm is needed to infer trustworthiness and help users gain valuable information.

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“…Such systems use the knowledge of a trust network among users, to provide personalized recommendations by aggregating the opinions of their trusted friends. Several models have been proposed to aggregate trust information among trusted friends [7], such as TidalTrust [8], MoleTrust [9], FlowTrust [10], and RN-Trust [11]. These models work in one round, i.e., only the current trust information is considered, or, the information is taken as static.…”

Section: Introductionmentioning

confidence: 99%

FluidRating: A time-evolving rating scheme in trust-based recommendation systems using fluid dynamics

Jiang

Wang

et al. 2014

IEEE INFOCOM 2014 - IEEE Conference on Computer Communications

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Abstract-The goal of a trust-based recommendation system is to predict unknown ratings based on the ratings expressed by trusted friends. However, most of the existing work only considers the ratings at the current time slot. In real life, a user receives the influence of different opinions sequentially; accordingly, his opinion evolves over time. We propose a novel rating prediction scheme, FluidRating, which uses fluid dynamics theory to reveal the time-evolving formulation process of human opinions. The recommendation is modeled as fluid with two dimensions: the temperature is taken as the "opinion/rating," and its volume is deemed as the "persistency," representing how much one insists on his opinion. When new opinions come, each user refines his opinion through a round of fluid exchange with his neighbors. Opinions from multiple rounds are aggregated to gain a final prediction; both uniform and non-uniform aggregation are tested. Moreover, Three sampling approaches are proposed and examined. The experimental evaluation of a real data set validates the feasibility of the proposed model, and also demonstrates its effectiveness.

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Trust Inference in Web-Based Social Networks Using Resistive Networks

Cited by 42 publications

References 10 publications

Learning influence probabilities in social networks

Learning influence probabilities in social networks

Current Trust Inference Mechanisms in Web Based Social Networks

FluidRating: A time-evolving rating scheme in trust-based recommendation systems using fluid dynamics

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