A Survey on the Recent Advances of Deep Community Detection
In the first days of social networking, the typical view of a community was a set of user profiles of the same interests and likes, and this community kept enlarging by searching, proposing, and adding new members with the same characteristics that were likely to interfere with the existing members....
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/16/7179 |
| _version_ | 1797524837967593472 |
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| author | Stavros Souravlas Sofia Anastasiadou Stefanos Katsavounis |
| author_facet | Stavros Souravlas Sofia Anastasiadou Stefanos Katsavounis |
| author_sort | Stavros Souravlas |
| collection | DOAJ |
| description | In the first days of social networking, the typical view of a community was a set of user profiles of the same interests and likes, and this community kept enlarging by searching, proposing, and adding new members with the same characteristics that were likely to interfere with the existing members. Today, things have changed dramatically. Social networking platforms are not restricted to forming similar user profiles: The vast amounts of data produced every day have given opportunities to predict and suggest relationships, behaviors, and everyday activities like shopping, food, traveling destinations, etc. Every day, vast data amounts are generated by the famous social networks such as Facebook, Twitter, Instagram, and so on. For example, Facebook alone generates 4 petabytes of data per day. The analysis of such data is of high importance to many aspects like recommendation systems, businesses, health organizations, etc. The community detection problem received considerable attention a long time ago. Communities are represented as clusters of an entire network. Most of the community detection techniques are based on graph structures. In this paper, we present the recent advances of deep learning techniques for community detection. We describe the most recent strategies presented in this field, and we provide some general discussion and some future trends. |
| first_indexed | 2024-03-10T09:02:20Z |
| format | Article |
| id | doaj.art-58922210ab324fbca3fb5da5683c79e2 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T09:02:20Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-58922210ab324fbca3fb5da5683c79e22023-11-22T06:37:40ZengMDPI AGApplied Sciences2076-34172021-08-011116717910.3390/app11167179A Survey on the Recent Advances of Deep Community DetectionStavros Souravlas0Sofia Anastasiadou1Stefanos Katsavounis2Department of Applied Informatics, School of Information Sciences, University of Macedonia Thessaloniki, PC 54616 Thessaloniki, GreeceDepartment of Midwifery, School of Health Sciences, University of Western Macedonia, PC 50200 Ptolemaida, GreeceDepartment of Production and Management Engineering, Democritus University of Thrace, PC 67100 Xanthi, GreeceIn the first days of social networking, the typical view of a community was a set of user profiles of the same interests and likes, and this community kept enlarging by searching, proposing, and adding new members with the same characteristics that were likely to interfere with the existing members. Today, things have changed dramatically. Social networking platforms are not restricted to forming similar user profiles: The vast amounts of data produced every day have given opportunities to predict and suggest relationships, behaviors, and everyday activities like shopping, food, traveling destinations, etc. Every day, vast data amounts are generated by the famous social networks such as Facebook, Twitter, Instagram, and so on. For example, Facebook alone generates 4 petabytes of data per day. The analysis of such data is of high importance to many aspects like recommendation systems, businesses, health organizations, etc. The community detection problem received considerable attention a long time ago. Communities are represented as clusters of an entire network. Most of the community detection techniques are based on graph structures. In this paper, we present the recent advances of deep learning techniques for community detection. We describe the most recent strategies presented in this field, and we provide some general discussion and some future trends.https://www.mdpi.com/2076-3417/11/16/7179deep learningnetwork analysiscommunity detectionsocial computing |
| spellingShingle | Stavros Souravlas Sofia Anastasiadou Stefanos Katsavounis A Survey on the Recent Advances of Deep Community Detection Applied Sciences deep learning network analysis community detection social computing |
| title | A Survey on the Recent Advances of Deep Community Detection |
| title_full | A Survey on the Recent Advances of Deep Community Detection |
| title_fullStr | A Survey on the Recent Advances of Deep Community Detection |
| title_full_unstemmed | A Survey on the Recent Advances of Deep Community Detection |
| title_short | A Survey on the Recent Advances of Deep Community Detection |
| title_sort | survey on the recent advances of deep community detection |
| topic | deep learning network analysis community detection social computing |
| url | https://www.mdpi.com/2076-3417/11/16/7179 |
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