A Relationships-based Algorithm for Detecting the Communities in Social Networks
Social network research analyzes the relationships between interactions, people, organizations, and entities. With the developing reputation of social media, community detection is drawing the attention of researchers. The purpose of community detection is to divide social networks into groups. Thes...
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| Format: | Article |
| Language: | English |
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University of science and culture
2022-07-01
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| Series: | International Journal of Web Research |
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| Online Access: | https://ijwr.usc.ac.ir/article_164089_d32701b7605a2513601952fcadb8e01f.pdf |
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| author | Sevda Fotovvat Habib Izadkhah Javad Hajipour |
| author_facet | Sevda Fotovvat Habib Izadkhah Javad Hajipour |
| author_sort | Sevda Fotovvat |
| collection | DOAJ |
| description | Social network research analyzes the relationships between interactions, people, organizations, and entities. With the developing reputation of social media, community detection is drawing the attention of researchers. The purpose of community detection is to divide social networks into groups. These communities are made of entities that are very closely related. Communities are defined as groups of nodes or summits that have strong relationships among themselves rather than between themselves. The clustering of social networks is important for revealing the basic structures of social networks and discovering the hyperlink of systems on human beings and their interactions. Social networks can be represented by graphs where users are shown with the nodes of the graph and the relationships between the users are shown with the edges. Communities are detected through clustering algorithms. In this paper, we proposed a new clustering algorithm that takes into account the extent of relationships among people. Outcomes from particular data suggest that taking into account the profundity of people-to-people relationships increases the correctness of the aggregation methods. |
| format | Article |
| id | doaj-art-004ca52f9e04401c98cfda7146f93b55 |
| institution | Kabale University |
| issn | 2645-4343 |
| language | English |
| publishDate | 2022-07-01 |
| publisher | University of science and culture |
| record_format | Article |
| series | International Journal of Web Research |
| spelling | doaj-art-004ca52f9e04401c98cfda7146f93b552025-01-04T08:09:31ZengUniversity of science and cultureInternational Journal of Web Research2645-43432022-07-01521810.22133/ijwr.2022.347854.1124A Relationships-based Algorithm for Detecting the Communities in Social NetworksSevda Fotovvat0Habib Izadkhah 1Javad Hajipour2Department of Computer Science, University of Tabriz, Tabriz, IranDepartment of Computer Science, University of Tabriz, Tabriz, IranDepartment of Computer Science, University of Tabriz, Tabriz, IranSocial network research analyzes the relationships between interactions, people, organizations, and entities. With the developing reputation of social media, community detection is drawing the attention of researchers. The purpose of community detection is to divide social networks into groups. These communities are made of entities that are very closely related. Communities are defined as groups of nodes or summits that have strong relationships among themselves rather than between themselves. The clustering of social networks is important for revealing the basic structures of social networks and discovering the hyperlink of systems on human beings and their interactions. Social networks can be represented by graphs where users are shown with the nodes of the graph and the relationships between the users are shown with the edges. Communities are detected through clustering algorithms. In this paper, we proposed a new clustering algorithm that takes into account the extent of relationships among people. Outcomes from particular data suggest that taking into account the profundity of people-to-people relationships increases the correctness of the aggregation methods.https://ijwr.usc.ac.ir/article_164089_d32701b7605a2513601952fcadb8e01f.pdfsocial networkscomplex networkscommunity detectioncommunity sensinggraph clustering |
| spellingShingle | Sevda Fotovvat Habib Izadkhah Javad Hajipour A Relationships-based Algorithm for Detecting the Communities in Social Networks International Journal of Web Research social networks complex networks community detection community sensing graph clustering |
| title | A Relationships-based Algorithm for Detecting the Communities in Social Networks |
| title_full | A Relationships-based Algorithm for Detecting the Communities in Social Networks |
| title_fullStr | A Relationships-based Algorithm for Detecting the Communities in Social Networks |
| title_full_unstemmed | A Relationships-based Algorithm for Detecting the Communities in Social Networks |
| title_short | A Relationships-based Algorithm for Detecting the Communities in Social Networks |
| title_sort | relationships based algorithm for detecting the communities in social networks |
| topic | social networks complex networks community detection community sensing graph clustering |
| url | https://ijwr.usc.ac.ir/article_164089_d32701b7605a2513601952fcadb8e01f.pdf |
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