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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...

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Main Authors: Rehman, Muhammad Zubair, Kamal Zuhairi, Zamli, Almutairi, Mubarak, Chiroma, Haruna, Aamir, Muhammad, Kader, Md. Abdul, Nazri, Mohd. Nawi
Format: Article
Language:English
Published: Public Library of Science 2021
Subjects:
QA75 Electronic computers. Computer science
QA76 Computer software
Online Access:http://umpir.ump.edu.my/id/eprint/33087/1/A%20novel%20state%20space%20reduction%20algorithm%20for%20team%20formation%20in%20social%20networks.pdf
http://umpir.ump.edu.my/id/eprint/33087/
https://doi.org/10.1371/journal.pone.0259786
https://doi.org/10.1371/journal.pone.0259786
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spelling my.ump.umpir.330872022-05-31T02:03:59Z 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 https://doi.org/10.1371/journal.pone.0259786 https://doi.org/10.1371/journal.pone.0259786
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA75 Electronic computers. Computer science
QA76 Computer software
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
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.
format Article
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
title 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_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_sort novel state space reduction algorithm for team formation in social networks
publisher Public Library of Science
publishDate 2021
url http://umpir.ump.edu.my/id/eprint/33087/1/A%20novel%20state%20space%20reduction%20algorithm%20for%20team%20formation%20in%20social%20networks.pdf
http://umpir.ump.edu.my/id/eprint/33087/
https://doi.org/10.1371/journal.pone.0259786
https://doi.org/10.1371/journal.pone.0259786
_version_ 1735387003289075712
score 13.211869

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