Application of logical differential calculus and binary decision diagramin importance analysis

Zaitseva, Elena; Levashenko, Vitaly; Kostolny, Jozef

doi:10.17531/ein.2015.3.8

Cited by 5 publications

(4 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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]. In this paper, the Danesh et al…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…In [10], a matrix is used to represent the direct trust relations among users of a social network, and a breadth-first search is performed to update users' trustworthiness in an iterative manner. In [4], a binary decision diagrams-based method [29]- [31] was proposed to evaluate the trust between two parties, but it is only applicable to the binary trust model.…”

Section: Related Workmentioning

confidence: 99%

Multivalued Decision Diagrams-Based Trust Level Analysis for Social Networks

et al. 2019

View full text Add to dashboard Cite

Interactions among different parties within social networks are greatly dependent on trust. Therefore, trust analysis is significant for solving social network related problems such as privacy protect, and rumor tracking and containment. This paper makes advancements in the trust analysis by proposing a reliability model-based algorithm for assessing the trust level of any two parties within a social network. Particularly, a multi-level trust model with the probability distribution is proposed and a multivalued decision diagrams (MDD)-based method is suggested for assessing the trust level of two parties that may be connected through multiple indirect or direct links. These connection paths may be correlated due to sharing a common party or link. Further, the MDD-based method is extended for performing a trust sensitivity analysis with the aim to pinpoint which direct link contributes the most to the trust relationship between two non-directly connected parties within the social network. Dynamics in trust are also investigated. Examples are provided to illustrate the proposed probabilistic MDD-based method for trust and sensitivity analyses. Performance of the proposed method is evaluated through experiments and comparisons with existing trust evaluation methods. INDEX TERMS Multivalued decision diagram, reliability model, sensitivity analysis, trust analysis.

show abstract

“…The blackboard model is widely used for establishing multi-agent intelligent systems [2,20,23], especially for the ones with dynamic, uncertain and complex tasks. It provides a flexible and efficient method of communication among agents.…”

Section: Blackboard Modelmentioning

confidence: 99%

An autonomous multi-sensor satellite system based on multi-agent blackboard model

He¹,

Li²,

Xing³

et al. 2017

EiN

View full text Add to dashboard Cite

Application of logical differential calculus and binary decision diagramin importance analysis

Cited by 5 publications

References 29 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

Multivalued Decision Diagrams-Based Trust Level Analysis for Social Networks

An autonomous multi-sensor satellite system based on multi-agent blackboard model

Contact Info

Product

Resources

About