Parallel and distributed paradigms for community detection in social networks: A methodological review
[Display omitted] •Systematic procedures are followed to obtain the most relevant studies.•Mendeley web application is used in filtering process to extract the relevant studies.•Each relevant study is evaluated based on the performance analysis questions.•Selected studies are compared based on the v...
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| Published in | Expert systems with applications Vol. 187; p. 115956 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Elsevier Ltd
01.01.2022
Elsevier BV |
| Subjects | |
| Online Access | Get full text |
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| Abstract | [Display omitted]
•Systematic procedures are followed to obtain the most relevant studies.•Mendeley web application is used in filtering process to extract the relevant studies.•Each relevant study is evaluated based on the performance analysis questions.•Selected studies are compared based on the various important parameters.•The articles are categorized based on the type of framework used for implementations.
Community detection in social networks is the process of identifying the cohesive groups of similar nodes. Detection of these groups can be helpful in many applications, such as finding networks of protein interaction in biological networks, finding the users of similar mind for ads and suggestions, finding a shared research field in collaborative networks, analyzing public health, future link prediction in social networks, analyzing criminology, and many more. However, with the increase in the number of profiles and content shared on social media platforms, the analysis is often time-consuming and exhaustive. In order to speed up and optimize the community detection process, parallel processing and Shared/Distributed memory techniques are widely used. Despite community detection has widespread use in social networks, no attempt has ever been made to compile and systematically discuss research efforts on the emerging subject of identifying parallel and distributed methods for community detection in social networks. Most of the surveys described the serial algorithms used for community detection. Our survey work comes under the scope of new design techniques, exciting or novel applications, components or standards, and applications of an educational, transactional, and co-operational nature. This paper accommodates and presents a systematic literature review with state-of-the-art research on the application of parallel processing and Shared/Distributed techniques to determine communities for social network analysis. Advanced search strategy has been performed on several digital libraries for extracting several studies for the review. The systematic search landed in finding 3220 studies, among which 65 relevant studies are selected after conducting various screening phases for further review. The application of parallel computing, shared memory, and distributed memory on the existing community detection methodologies have been discussed thoroughly. More specifically, the central significance of this paper is that a systematic literature review is conducted to gather the relevant studies from different digital libraries and gray literature. Then, different parametric values of each selected study are appropriately compared. Moreover, the need for further research to speed up the process of community formation in parallel and shared approaches has been pinpointed more suitably. A pictorial glance of this paper is depicted as follows: |
|---|---|
| AbstractList | [Display omitted]
•Systematic procedures are followed to obtain the most relevant studies.•Mendeley web application is used in filtering process to extract the relevant studies.•Each relevant study is evaluated based on the performance analysis questions.•Selected studies are compared based on the various important parameters.•The articles are categorized based on the type of framework used for implementations.
Community detection in social networks is the process of identifying the cohesive groups of similar nodes. Detection of these groups can be helpful in many applications, such as finding networks of protein interaction in biological networks, finding the users of similar mind for ads and suggestions, finding a shared research field in collaborative networks, analyzing public health, future link prediction in social networks, analyzing criminology, and many more. However, with the increase in the number of profiles and content shared on social media platforms, the analysis is often time-consuming and exhaustive. In order to speed up and optimize the community detection process, parallel processing and Shared/Distributed memory techniques are widely used. Despite community detection has widespread use in social networks, no attempt has ever been made to compile and systematically discuss research efforts on the emerging subject of identifying parallel and distributed methods for community detection in social networks. Most of the surveys described the serial algorithms used for community detection. Our survey work comes under the scope of new design techniques, exciting or novel applications, components or standards, and applications of an educational, transactional, and co-operational nature. This paper accommodates and presents a systematic literature review with state-of-the-art research on the application of parallel processing and Shared/Distributed techniques to determine communities for social network analysis. Advanced search strategy has been performed on several digital libraries for extracting several studies for the review. The systematic search landed in finding 3220 studies, among which 65 relevant studies are selected after conducting various screening phases for further review. The application of parallel computing, shared memory, and distributed memory on the existing community detection methodologies have been discussed thoroughly. More specifically, the central significance of this paper is that a systematic literature review is conducted to gather the relevant studies from different digital libraries and gray literature. Then, different parametric values of each selected study are appropriately compared. Moreover, the need for further research to speed up the process of community formation in parallel and shared approaches has been pinpointed more suitably. A pictorial glance of this paper is depicted as follows: Community detection in social networks is the process of identifying the cohesive groups of similar nodes. Detection of these groups can be helpful in many applications, such as finding networks of protein interaction in biological networks, finding the users of similar mind for ads and suggestions, finding a shared research field in collaborative networks, analyzing public health, future link prediction in social networks, analyzing criminology, and many more. However, with the increase in the number of profiles and content shared on social media platforms, the analysis is often time-consuming and exhaustive. In order to speed up and optimize the community detection process, parallel processing and Shared/Distributed memory techniques are widely used. Despite community detection has widespread use in social networks, no attempt has ever been made to compile and systematically discuss research efforts on the emerging subject of identifying parallel and distributed methods for community detection in social networks. Most of the surveys described the serial algorithms used for community detection. Our survey work comes under the scope of new design techniques, exciting or novel applications, components or standards, and applications of an educational, transactional, and co-operational nature. This paper accommodates and presents a systematic literature review with state-of-the-art research on the application of parallel processing and Shared/Distributed techniques to determine communities for social network analysis. Advanced search strategy has been performed on several digital libraries for extracting several studies for the review. The systematic search landed in finding 3220 studies, among which 65 relevant studies are selected after conducting various screening phases for further review. The application of parallel computing, shared memory, and distributed memory on the existing community detection methodologies have been discussed thoroughly. More specifically, the central significance of this paper is that a systematic literature review is conducted to gather the relevant studies from different digital libraries and gray literature. Then, different parametric values of each selected study are appropriately compared. Moreover, the need for further research to speed up the process of community formation in parallel and shared approaches has been pinpointed more suitably. A pictorial glance of this paper is depicted as follows: |
| ArticleNumber | 115956 |
| Author | Naik, Debadatta Gandomi, Amir H. Ramesh, Dharavath Babu Gorojanam, Naveen |
| Author_xml | – sequence: 1 givenname: Debadatta surname: Naik fullname: Naik, Debadatta organization: Department of Computer Science & Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, India – sequence: 2 givenname: Dharavath surname: Ramesh fullname: Ramesh, Dharavath email: drramesh@iitism.ac.in organization: Department of Computer Science & Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, India – sequence: 3 givenname: Amir H. surname: Gandomi fullname: Gandomi, Amir H. organization: Department of Engineering & Information Technology, University of Technology Sydney, Ultimo, NSW, Australia – sequence: 4 givenname: Naveen surname: Babu Gorojanam fullname: Babu Gorojanam, Naveen organization: Department of Computer Science & Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, India |
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•Systematic procedures are followed to obtain the most relevant studies.•Mendeley web application is used in filtering process to extract the... Community detection in social networks is the process of identifying the cohesive groups of similar nodes. Detection of these groups can be helpful in many... |
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| Title | Parallel and distributed paradigms for community detection in social networks: A methodological review |
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