OpinionWalk: An efficient solution to massive trust assessment in online social networks

Liu, Guangchi; Chen, Qi; Yang, Qing; Zhu, Binhai; Wang, Honggang; Wang, Wei

doi:10.1109/infocom.2017.8057106

Cited by 46 publications

(17 citation statements)

References 16 publications

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“…Reference [11] uses a matrix to display direct trust relationships between users on a social network and uses the Breadth-First search algorithm to enhance users trust. A binary decision chart based method [13] is proposed to evaluate the trust between the two parties, but it is only applicable for the binary trust model in the reference [12].…”

Section: Previous Work Reviewmentioning

confidence: 99%

Predicting Trust in a Social Network Based on Structural Similarities Using a Multi-Layered Perceptron Neural Network

Danesh

Shirgahi

2021

IIUMEJ

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Although research on social networks is progressing rapidly, the positive and negative effects of this area should be evaluated. One of the problems is that social networks are very broad and anyone can have influence on them. This matter can cause the issue of people with different beliefs. Therefore, determining the amount of trust to various resources on social networks, and especially resources for which there is no previous history on the web, is one of the main challenges in this field. In this paper, we present a method for predicting trust in a social network by structural similarities through the neural network. In this method, the web of trust data set is converted to a structural similarity data set based on the similarity of the trustors and trustees first. Then, on the created data set, a part of the data set is considered as the training data and it is trained based on the multilayer perceptron neural network and then the trained neural network is tested based on the test data. In the proposed method, the MSE value is less than 0.01, which has improved more than 0.02 compared to previous methods. Based on the obtained results, the proposed method has provided acceptable accuracy. ABSTRAK: Walaupun kajian tentang rangkaian sosial adalah sangat pesat, kesan positif dan negatif dalam ruang lingkup ini perlu dinilai. Masalah rangkaian sosial adalah sangat luas dan sesiapa sahaja boleh terpengaruh. Perkara ini akan menyebabkan manusia dengan pelbagai isu kepercayaan. Oleh itu, menentukan nilai kepercayaan melalui pelbagai sumber dalam rangkaian sosial, terutama sumber-sumber yang tidak mempunyai sejarah lepas dalam web, adalah salah satu cabaran dalam bidang ini. Kajian ini membentangkan jangkaan kepercayaan dalam rangkaian sosial melalui persamaan struktur dengan menggunakan rangkaian neural. Kaedah ini ditentukan dengan menukar set data web kepercayaan kepada struktur set data hampir sama berdasarkan kesamaan pemegang dan pemberi amanah. Kemudian, sebilangan set data yang telah dibina ini dipertimbangkan sebagai data latihan dan ia dilatih berdasarkan rangkaian neural perseptron berbagai lapisan dan kemudian rangkaian neural yang terlatih ini diuji berdasarkan data ujian. Dalam kaedah yang dicadangkan ini, nilai MSE adalah kurang daripada 0.01, di mana telah diperbaiki kepada 0.02 lebih daripada kaedah-kaedah sebelum ini. Berdasarkan dapatan kajian, didapati kaedah yang dicadangkan ini menunjukkan ketepatan yang boleh diterima.

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Section: Previous Work Reviewmentioning

confidence: 99%

Predicting Trust in a Social Network Based on Structural Similarities Using a Multi-Layered Perceptron Neural Network

Danesh

Shirgahi

2021

IIUMEJ

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“…In wireless network domain, trust can help a cellular device to discover trustworthy peers to relay its data [5,6]. In security domain, trust is considered an important metric to detect malicious users or websites [7,8,9]. Given the abovementioned applications, one confounding issue is to what degree a user can trust another user in an OSN.…”

Section: Introductionmentioning

confidence: 99%

NeuralWalk: Trust Assessment in Online Social Networks with Neural Networks

Liu¹,

Li²,

Yang

2019

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications

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Assessing trust in online social networks (OSNs) is critical for many applications such as online marketing and network security. It is a challenging problem, however, due to the difficulties of handling complex social network topologies and conducting accurate assessment in these topologies. To address these challenges, we model trust by proposing the three-valued subjective logic (3VSL) model. 3VSL properly models the uncertainties that exist in trust, thus is able to compute trust in arbitrary graphs. We theoretically prove the capability of 3VSL based on the Dirichlet-Categorical (DC) distribution and its correctness in arbitrary OSN topologies. Based on the 3VSL model, we further design the AssessTrust (AT) algorithm to accurately compute the trust between any two users connected in an OSN. We validate 3VSL against two real-world OSN datasets: Advogato and Pretty Good Privacy (PGP). Experimental results indicate that 3VSL can accurately model the trust between any pair of indirectly connected users in the Advogato and PGP.

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“…Each is then analyzed theoretically and with respect to simulated and actual trust networks. Liu et al [13] proposed the OpinionWalk algorithm that models trust by the Dirichlet distribution and uses a matrix to represent the direct trust relations among users. There have also been many studies on social network trust; examples can be seen in Richardson et al [14], Ziegler and Lausen [15], Quercia et al [16], and Hang et al [17].…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis of Multi-State Social Network System Based on MDD Method

2019

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With the advancement of computer and network technologies, Internet-based social networks called social networking services have become popular. Trust is a crucial basis for interactions among parties in social networks. Based on trust scores of direct links between parties, a trust sensitivity analysis can help identify which direct link(s) in a social network contributes the most to a trust relationship between parties who are not directly connected in the network. This paper generalizes the research object from two-state social networks to multistate social networks since the trust grade for people in a real social connection may have multiple levels. We model asymmetric multitrust level and multiparty social network systems and propose a probabilistic method based on multivalued decision diagrams (MDDs) to perform trust sensitivity analysis of social networks. Numerical examples are provided to demonstrate the application of the proposed methodology.

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OpinionWalk: An efficient solution to massive trust assessment in online social networks

Cited by 46 publications

References 16 publications

Predicting Trust in a Social Network Based on Structural Similarities Using a Multi-Layered Perceptron Neural Network

Predicting Trust in a Social Network Based on Structural Similarities Using a Multi-Layered Perceptron Neural Network

NeuralWalk: Trust Assessment in Online Social Networks with Neural Networks

Sensitivity Analysis of Multi-State Social Network System Based on MDD Method

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