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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Science and Research Branch,Islamic Azad University
2018-02-01
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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 |
_version_ |
1725208826586595328 |