A novel state space reduction algorithm for team formation in social networks
Team formation (TF) in social networks exploits graphs (i.e., vertices = experts and edges = skills) to represent a possible collaboration between the experts. These networks lead us towards building cost-effective research teams irrespective of the geolocation of the experts and the size of the dat...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science
2021
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/33087/1/A%20novel%20state%20space%20reduction%20algorithm%20for%20team%20formation%20in%20social%20networks.pdf |
| _version_ | 1825814145914634240 |
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| author | Rehman, Muhammad Zubair Kamal Zuhairi, Zamli Almutairi, Mubarak Chiroma, Haruna Aamir, Muhammad Kader, Md. Abdul Nazri, Mohd. Nawi |
| author_facet | Rehman, Muhammad Zubair Kamal Zuhairi, Zamli Almutairi, Mubarak Chiroma, Haruna Aamir, Muhammad Kader, Md. Abdul Nazri, Mohd. Nawi |
| author_sort | Rehman, Muhammad Zubair |
| collection | UMP |
| description | Team formation (TF) in social networks exploits graphs (i.e., vertices = experts and edges = skills) to represent a possible collaboration between the experts. These networks lead us towards building cost-effective research teams irrespective of the geolocation of the experts and the size of the dataset. Previously, large datasets were not closely inspected for the large-scale distributions & relationships among the researchers, resulting in the algorithms failing to scale well on the data. Therefore, this paper presents a novel TF algorithm for expert team formation called SSR-TF based on two metrics; communication cost and graph reduction, that will become a basis for future TF’s. In SSR-TF, communication cost finds the possibility of collaboration between researchers. The graph reduction scales the large data to only appropriate skills and the experts, resulting in real-time extraction of experts for collaboration. This approach is tested on five organic and benchmark datasets, i.e., UMP, DBLP, ACM, IMDB, and Bibsonomy. The SSR-TF algorithm is able to build cost-effective teams with the most appropriate experts–resulting in the formation of more communicative teams with high expertise levels. |
| first_indexed | 2024-03-06T12:54:32Z |
| format | Article |
| id | UMPir33087 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:54:32Z |
| publishDate | 2021 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | UMPir330872022-05-31T02:03:59Z http://umpir.ump.edu.my/id/eprint/33087/ A novel state space reduction algorithm for team formation in social networks Rehman, Muhammad Zubair Kamal Zuhairi, Zamli Almutairi, Mubarak Chiroma, Haruna Aamir, Muhammad Kader, Md. Abdul Nazri, Mohd. Nawi QA75 Electronic computers. Computer science QA76 Computer software Team formation (TF) in social networks exploits graphs (i.e., vertices = experts and edges = skills) to represent a possible collaboration between the experts. These networks lead us towards building cost-effective research teams irrespective of the geolocation of the experts and the size of the dataset. Previously, large datasets were not closely inspected for the large-scale distributions & relationships among the researchers, resulting in the algorithms failing to scale well on the data. Therefore, this paper presents a novel TF algorithm for expert team formation called SSR-TF based on two metrics; communication cost and graph reduction, that will become a basis for future TF’s. In SSR-TF, communication cost finds the possibility of collaboration between researchers. The graph reduction scales the large data to only appropriate skills and the experts, resulting in real-time extraction of experts for collaboration. This approach is tested on five organic and benchmark datasets, i.e., UMP, DBLP, ACM, IMDB, and Bibsonomy. The SSR-TF algorithm is able to build cost-effective teams with the most appropriate experts–resulting in the formation of more communicative teams with high expertise levels. Public Library of Science 2021-12 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/33087/1/A%20novel%20state%20space%20reduction%20algorithm%20for%20team%20formation%20in%20social%20networks.pdf Rehman, Muhammad Zubair and Kamal Zuhairi, Zamli and Almutairi, Mubarak and Chiroma, Haruna and Aamir, Muhammad and Kader, Md. Abdul and Nazri, Mohd. Nawi (2021) A novel state space reduction algorithm for team formation in social networks. PLoS ONE, 16 (12). pp. 1-18. ISSN 1932-6203. (Published) https://doi.org/10.1371/journal.pone.0259786 https://doi.org/10.1371/journal.pone.0259786 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software Rehman, Muhammad Zubair Kamal Zuhairi, Zamli Almutairi, Mubarak Chiroma, Haruna Aamir, Muhammad Kader, Md. Abdul Nazri, Mohd. Nawi A novel state space reduction algorithm for team formation in social networks |
| title | A novel state space reduction algorithm for team formation in social networks |
| title_full | A novel state space reduction algorithm for team formation in social networks |
| title_fullStr | A novel state space reduction algorithm for team formation in social networks |
| title_full_unstemmed | A novel state space reduction algorithm for team formation in social networks |
| title_short | A novel state space reduction algorithm for team formation in social networks |
| title_sort | novel state space reduction algorithm for team formation in social networks |
| topic | QA75 Electronic computers. Computer science QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/33087/1/A%20novel%20state%20space%20reduction%20algorithm%20for%20team%20formation%20in%20social%20networks.pdf |
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