Local Community Detection: A Survey
Community detection is a flourishing research field with a plethora of applications ranging from biology to sociology. Local community detection has emerged as a promising subfield of research concerned with community identification around a set of seeding nodes. The practical significance of local...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9916271/ |
| _version_ | 1828096115301416960 |
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| author | Georgia Baltsou Konstantinos Christopoulos Konstantinos Tsichlas |
| author_facet | Georgia Baltsou Konstantinos Christopoulos Konstantinos Tsichlas |
| author_sort | Georgia Baltsou |
| collection | DOAJ |
| description | Community detection is a flourishing research field with a plethora of applications ranging from biology to sociology. Local community detection has emerged as a promising subfield of research concerned with community identification around a set of seeding nodes. The practical significance of local community detection is important for numerous real-world applications such as protein interactions and targeted advertising. Since 2005, when the first research paper on local community detection appeared, the literature has been vast and difficult to navigate, as each method works best under certain conditions and assumptions regarding the seed nodes and the identification of their community. For this reason, and motivated by the many real-world applications of local community detection, in this paper we provide a comprehensive overview and taxonomy of local community detection algorithms. There are quite a lot of surveys on community detection that make a compendious reference to local community detection. However, they do not achieve a systematic and comprehensive coverage of this particular field. Since the research area of local community detection is quite extensive, it is necessary to categorize and discuss the various methods, techniques, and assumptions used to address the problem. This survey aims to fill this gap and help researchers get a clear overview of the local community detection problem. To this end, we have also gathered the best documented tools and the most commonly used datasets in the local community detection literature to help researchers identify the tools they can use to prove their methods. |
| first_indexed | 2024-04-11T07:25:09Z |
| format | Article |
| id | doaj.art-2c06f676a6814d08b233a5fca2e24444 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T07:25:09Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2c06f676a6814d08b233a5fca2e244442022-12-22T04:37:06ZengIEEEIEEE Access2169-35362022-01-011011070111072610.1109/ACCESS.2022.32139809916271Local Community Detection: A SurveyGeorgia Baltsou0https://orcid.org/0000-0002-7042-8876Konstantinos Christopoulos1https://orcid.org/0000-0002-1246-7236Konstantinos Tsichlas2Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, GreeceDepartment of Computer Engineering and Informatics, University of Patras, Patras, GreeceDepartment of Computer Engineering and Informatics, University of Patras, Patras, GreeceCommunity detection is a flourishing research field with a plethora of applications ranging from biology to sociology. Local community detection has emerged as a promising subfield of research concerned with community identification around a set of seeding nodes. The practical significance of local community detection is important for numerous real-world applications such as protein interactions and targeted advertising. Since 2005, when the first research paper on local community detection appeared, the literature has been vast and difficult to navigate, as each method works best under certain conditions and assumptions regarding the seed nodes and the identification of their community. For this reason, and motivated by the many real-world applications of local community detection, in this paper we provide a comprehensive overview and taxonomy of local community detection algorithms. There are quite a lot of surveys on community detection that make a compendious reference to local community detection. However, they do not achieve a systematic and comprehensive coverage of this particular field. Since the research area of local community detection is quite extensive, it is necessary to categorize and discuss the various methods, techniques, and assumptions used to address the problem. This survey aims to fill this gap and help researchers get a clear overview of the local community detection problem. To this end, we have also gathered the best documented tools and the most commonly used datasets in the local community detection literature to help researchers identify the tools they can use to prove their methods.https://ieeexplore.ieee.org/document/9916271/Algorithmscommunity detectionlocalsurvey |
| spellingShingle | Georgia Baltsou Konstantinos Christopoulos Konstantinos Tsichlas Local Community Detection: A Survey IEEE Access Algorithms community detection local survey |
| title | Local Community Detection: A Survey |
| title_full | Local Community Detection: A Survey |
| title_fullStr | Local Community Detection: A Survey |
| title_full_unstemmed | Local Community Detection: A Survey |
| title_short | Local Community Detection: A Survey |
| title_sort | local community detection a survey |
| topic | Algorithms community detection local survey |
| url | https://ieeexplore.ieee.org/document/9916271/ |
| work_keys_str_mv | AT georgiabaltsou localcommunitydetectionasurvey AT konstantinoschristopoulos localcommunitydetectionasurvey AT konstantinostsichlas localcommunitydetectionasurvey |