The Detection of Anomalous Users in Location-Based Social Networks by Using Graph Rules
An analysis of social networks is necessary to detect anomalous users, due to the popularity of these networks. This paper aims to detect anomalous users in location based social networks. For this purpose, an ego graph is computed for each user, and the five variables vertex degree, edge size, edge...
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| Format: | Article |
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University of Qom
2022-03-01
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| Series: | مدیریت مهندسی و رایانش نرم |
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| Online Access: | https://jemsc.qom.ac.ir/article_1373_39cfc86eabcbd0116fe42cc96098e3d9.pdf |
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| _version_ | 1832577582057390080 |
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| author | Fatemeh Edalati Morteza Romoozi |
| author_facet | Fatemeh Edalati Morteza Romoozi |
| author_sort | Fatemeh Edalati |
| collection | DOAJ |
| description | An analysis of social networks is necessary to detect anomalous users, due to the popularity of these networks. This paper aims to detect anomalous users in location based social networks. For this purpose, an ego graph is computed for each user, and the five variables vertex degree, edge size, edge weight, betweenness centrality and eigenvector centrality are calculated with respect to the weights of the edges in this graph. Then six relationships between two of these variables are made up, and for each of these relationships, the line equation is obtained in the coordinate system of the two variables. This equation is used to predict the value of the variables. Based on this predicted value, the user's score is determined, and anomalous users are detected. The proposed method investigates anomalies in the friendship graph, location of residence and interests of users. The results indicate that the proposed method has been able to detect anomalous users by examining the scores of star and clique structures in the graph. |
| format | Article |
| id | doaj-art-73d28e32e4db43ecb1ab45641779d5f0 |
| institution | Kabale University |
| issn | 2538-6239 2538-2675 |
| language | fas |
| publishDate | 2022-03-01 |
| publisher | University of Qom |
| record_format | Article |
| series | مدیریت مهندسی و رایانش نرم |
| spelling | doaj-art-73d28e32e4db43ecb1ab45641779d5f02025-01-30T20:18:14ZfasUniversity of Qomمدیریت مهندسی و رایانش نرم2538-62392538-26752022-03-018117118910.22091/jemsc.2019.13731373The Detection of Anomalous Users in Location-Based Social Networks by Using Graph RulesFatemeh Edalati0Morteza Romoozi1MSc. Student in software engineering, Kashan branch, Islamic Azad university, Kashan, IranAssistant Prof. faculty of computer and electrical engineering, Kashan branch, Islamic Azad university, Kashan, IranAn analysis of social networks is necessary to detect anomalous users, due to the popularity of these networks. This paper aims to detect anomalous users in location based social networks. For this purpose, an ego graph is computed for each user, and the five variables vertex degree, edge size, edge weight, betweenness centrality and eigenvector centrality are calculated with respect to the weights of the edges in this graph. Then six relationships between two of these variables are made up, and for each of these relationships, the line equation is obtained in the coordinate system of the two variables. This equation is used to predict the value of the variables. Based on this predicted value, the user's score is determined, and anomalous users are detected. The proposed method investigates anomalies in the friendship graph, location of residence and interests of users. The results indicate that the proposed method has been able to detect anomalous users by examining the scores of star and clique structures in the graph.https://jemsc.qom.ac.ir/article_1373_39cfc86eabcbd0116fe42cc96098e3d9.pdfanomaly detectionlocation-based social networksocial network analysis |
| spellingShingle | Fatemeh Edalati Morteza Romoozi The Detection of Anomalous Users in Location-Based Social Networks by Using Graph Rules مدیریت مهندسی و رایانش نرم anomaly detection location-based social network social network analysis |
| title | The Detection of Anomalous Users in Location-Based Social Networks by Using Graph Rules |
| title_full | The Detection of Anomalous Users in Location-Based Social Networks by Using Graph Rules |
| title_fullStr | The Detection of Anomalous Users in Location-Based Social Networks by Using Graph Rules |
| title_full_unstemmed | The Detection of Anomalous Users in Location-Based Social Networks by Using Graph Rules |
| title_short | The Detection of Anomalous Users in Location-Based Social Networks by Using Graph Rules |
| title_sort | detection of anomalous users in location based social networks by using graph rules |
| topic | anomaly detection location-based social network social network analysis |
| url | https://jemsc.qom.ac.ir/article_1373_39cfc86eabcbd0116fe42cc96098e3d9.pdf |
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