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

• 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

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.