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...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science
2021
|
| 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 http://umpir.ump.edu.my/id/eprint/33087/ https://doi.org/10.1371/journal.pone.0259786 https://doi.org/10.1371/journal.pone.0259786 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|