A novel state space reduction algorithm for team formation in social networks

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)

Pdf (Open Access)
A novel state space reduction algorithm for team formation in social networks.pdf
Available under License Creative Commons Attribution.

Download (1MB) | Preview


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.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Algorithm; Article; Extraction; Human; Skill; Social network
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Faculty/Division: Institute of Postgraduate Studies
Faculty of Computing
Depositing User: Mr Muhamad Firdaus Janih@Jaini
Date Deposited: 31 May 2022 02:03
Last Modified: 31 May 2022 02:03
URI: http://umpir.ump.edu.my/id/eprint/33087
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item