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Keane, Peter, Ghaffa, Faisal and Malone, David (2019) Using Machine Learning to Predict Links and Improve Steiner Tree Solutions to Team Formation Problems. In: The 8th International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2019). (In Press)
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Abstract
The team formation problem has existed for many years in various guises. One important problem in the team formation problem is to produce small teams that have a required set of skills. We propose a framework that incorporates machine learning to predict unobserved links between collaborators, alongside improved Steiner tree problems to form small teams to cover given tasks. Our framework not only considers size of the team but also how likely are team members going to collab-orate with each other. The results show that this model consistently returns smaller collaborative teams.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Keywords: | team formation; link prediction; Steiner tree; |
| Academic Unit: | Faculty of Science and Engineering > Mathematics and Statistics |
| Item ID: | 12079 |
| Identification Number: | 10.1007/978-3-030-36683-4_79 |
| Depositing User: | Dr. David Malone |
| Date Deposited: | 17 Dec 2019 10:52 |
| Refereed: | No |
| Funders: | Science Foundation Ireland |
| Related URLs: | |
| URI: | https://mu.eprints-hosting.org/id/eprint/12079 |
| Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
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