Influence propagation in social networks: Interest-based community ranking model
The driving force behind content dissemination in Social network (SN) is the users’ interest in the content, which is strongly reflected in their interactions. Obviously, user interest varies with the disseminated content. Consequently, the dynamic interest results in decomposing SN into dynamic use...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-05-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157820304304 |
| _version_ | 1811307214049116160 |
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| author | Nouran Ayman R. Abd Al-Azim Tarek F. Gharib Mohamed Hamdy Yasmine Afify |
| author_facet | Nouran Ayman R. Abd Al-Azim Tarek F. Gharib Mohamed Hamdy Yasmine Afify |
| author_sort | Nouran Ayman R. Abd Al-Azim |
| collection | DOAJ |
| description | The driving force behind content dissemination in Social network (SN) is the users’ interest in the content, which is strongly reflected in their interactions. Obviously, user interest varies with the disseminated content. Consequently, the dynamic interest results in decomposing SN into dynamic user clusters “interest groups”.The objective of this work is to rank interest-based communities using influence propagation. The contribution of this work is threefold: First, to highlight the significance of the indirect influence among interest-based user groups. Second, to study its impact on content dissemination capability. Third, to propose an ultimate ranking model (UltRank) that uniquely considers direct and indirect influences which are reflected in a new reachability metric that considers: 1. Distance among interest groups. 2. Percentage of reachable interest groups. 3. Percentage of reachable nodes.UltRank model has been evaluated in comprehensive experiments. First, clustering quality perspective, the Silhouette coefficient for the identified interest groups is on average 0.996 and the Jaccard coefficient of 97% of different interest groups members equals 0. Second, ranking capability perspective, UltRank model can rank up to 91% of interest groups in SN. Finally, ranking effectiveness perspective, UltRank ranking list has a competing network coverage results against the other benchmark approaches. |
| first_indexed | 2024-04-13T09:01:01Z |
| format | Article |
| id | doaj.art-5a4deabae9cb46eea669f88c1121dd64 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-04-13T09:01:01Z |
| publishDate | 2022-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-5a4deabae9cb46eea669f88c1121dd642022-12-22T02:53:09ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782022-05-0134522312243Influence propagation in social networks: Interest-based community ranking modelNouran Ayman R. Abd Al-Azim0Tarek F. Gharib1Mohamed Hamdy2Yasmine Afify3Information Systems Department, Faculty of Computer and Information Sciences, Ain Shams University, 11566 Cairo, EgyptCorresponding author.; Information Systems Department, Faculty of Computer and Information Sciences, Ain Shams University, 11566 Cairo, EgyptInformation Systems Department, Faculty of Computer and Information Sciences, Ain Shams University, 11566 Cairo, EgyptInformation Systems Department, Faculty of Computer and Information Sciences, Ain Shams University, 11566 Cairo, EgyptThe driving force behind content dissemination in Social network (SN) is the users’ interest in the content, which is strongly reflected in their interactions. Obviously, user interest varies with the disseminated content. Consequently, the dynamic interest results in decomposing SN into dynamic user clusters “interest groups”.The objective of this work is to rank interest-based communities using influence propagation. The contribution of this work is threefold: First, to highlight the significance of the indirect influence among interest-based user groups. Second, to study its impact on content dissemination capability. Third, to propose an ultimate ranking model (UltRank) that uniquely considers direct and indirect influences which are reflected in a new reachability metric that considers: 1. Distance among interest groups. 2. Percentage of reachable interest groups. 3. Percentage of reachable nodes.UltRank model has been evaluated in comprehensive experiments. First, clustering quality perspective, the Silhouette coefficient for the identified interest groups is on average 0.996 and the Jaccard coefficient of 97% of different interest groups members equals 0. Second, ranking capability perspective, UltRank model can rank up to 91% of interest groups in SN. Finally, ranking effectiveness perspective, UltRank ranking list has a competing network coverage results against the other benchmark approaches.http://www.sciencedirect.com/science/article/pii/S1319157820304304Influence propagationSocial networks analysisGraph miningCommunity rankingUltimate rank |
| spellingShingle | Nouran Ayman R. Abd Al-Azim Tarek F. Gharib Mohamed Hamdy Yasmine Afify Influence propagation in social networks: Interest-based community ranking model Journal of King Saud University: Computer and Information Sciences Influence propagation Social networks analysis Graph mining Community ranking Ultimate rank |
| title | Influence propagation in social networks: Interest-based community ranking model |
| title_full | Influence propagation in social networks: Interest-based community ranking model |
| title_fullStr | Influence propagation in social networks: Interest-based community ranking model |
| title_full_unstemmed | Influence propagation in social networks: Interest-based community ranking model |
| title_short | Influence propagation in social networks: Interest-based community ranking model |
| title_sort | influence propagation in social networks interest based community ranking model |
| topic | Influence propagation Social networks analysis Graph mining Community ranking Ultimate rank |
| url | http://www.sciencedirect.com/science/article/pii/S1319157820304304 |
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