Utilizes the Community Detection for Increase Trust using Multiplex Networks

Today, e-commerce has occupied a large volume of economic exchanges. It is known as one of the most effective business practices. Predicted trust which means trusting an anonymous user is important in online communities. In this paper, the trust was predicted by combining two methods of multiplex ne...

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Main Authors: Rahimeh Habibi, Ali Haroun Abadi
Format: Article
Language:English
Published: Science and Research Branch,Islamic Azad University 2018-02-01
Series:Journal of Advances in Computer Engineering and Technology
Subjects:
Online Access:http://jacet.srbiau.ac.ir/article_11309_3b06989667218963c502696dab080e6c.pdf
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spelling doaj-1a4045ce5ebb417899d86178eade539f2020-11-25T01:01:32ZengScience and Research Branch,Islamic Azad UniversityJournal of Advances in Computer Engineering and Technology2423-41922423-42062018-02-0141212611309Utilizes the Community Detection for Increase Trust using Multiplex NetworksRahimeh Habibi0Ali Haroun Abadi1student of ACRCE KhozestanDepatment of Computer, Central Tehran Branch, Islamic Azad University, Tehran, IranToday, e-commerce has occupied a large volume of economic exchanges. It is known as one of the most effective business practices. Predicted trust which means trusting an anonymous user is important in online communities. In this paper, the trust was predicted by combining two methods of multiplex network and community detection. In modeling the network in terms of a multiplex network, the relationships between users were different in each layer and each user had a rank in each layer. Then, the ratings of two layers including the weight of each layer were aggregated and four effective features of the Trust were achieved. Then, the network was divided into overlapping groups via community detection’ algorithms, each group representative was considered as the community centers and other features were extracted through similar comments. At the end, 48J decision tree algorithm was used to advance the work. The proposed method was assessed on Epinions data set and accuracy of trust was 96%.http://jacet.srbiau.ac.ir/article_11309_3b06989667218963c502696dab080e6c.pdfTrustCommunity DetectionMultiplex Networksocial network
collection DOAJ
language English
format Article
sources DOAJ
author Rahimeh Habibi
Ali Haroun Abadi
spellingShingle Rahimeh Habibi
Ali Haroun Abadi
Utilizes the Community Detection for Increase Trust using Multiplex Networks
Journal of Advances in Computer Engineering and Technology
Trust
Community Detection
Multiplex Network
social network
author_facet Rahimeh Habibi
Ali Haroun Abadi
author_sort Rahimeh Habibi
title Utilizes the Community Detection for Increase Trust using Multiplex Networks
title_short Utilizes the Community Detection for Increase Trust using Multiplex Networks
title_full Utilizes the Community Detection for Increase Trust using Multiplex Networks
title_fullStr Utilizes the Community Detection for Increase Trust using Multiplex Networks
title_full_unstemmed Utilizes the Community Detection for Increase Trust using Multiplex Networks
title_sort utilizes the community detection for increase trust using multiplex networks
publisher Science and Research Branch,Islamic Azad University
series Journal of Advances in Computer Engineering and Technology
issn 2423-4192
2423-4206
publishDate 2018-02-01
description Today, e-commerce has occupied a large volume of economic exchanges. It is known as one of the most effective business practices. Predicted trust which means trusting an anonymous user is important in online communities. In this paper, the trust was predicted by combining two methods of multiplex network and community detection. In modeling the network in terms of a multiplex network, the relationships between users were different in each layer and each user had a rank in each layer. Then, the ratings of two layers including the weight of each layer were aggregated and four effective features of the Trust were achieved. Then, the network was divided into overlapping groups via community detection’ algorithms, each group representative was considered as the community centers and other features were extracted through similar comments. At the end, 48J decision tree algorithm was used to advance the work. The proposed method was assessed on Epinions data set and accuracy of trust was 96%.
topic Trust
Community Detection
Multiplex Network
social network
url http://jacet.srbiau.ac.ir/article_11309_3b06989667218963c502696dab080e6c.pdf
work_keys_str_mv AT rahimehhabibi utilizesthecommunitydetectionforincreasetrustusingmultiplexnetworks
AT aliharounabadi utilizesthecommunitydetectionforincreasetrustusingmultiplexnetworks
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