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    Using machine learning to predict links and improve Steiner tree solutions to team formation problems - a cross company study


    Keane, Peter and Ghaffar, Faisal and Malone, David (2020) Using machine learning to predict links and improve Steiner tree solutions to team formation problems - a cross company study. Applied Network Science, 5 (1). ISSN 2364-8228

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    Abstract

    The team formation problem has existed for many years in various guises. One important challenge 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 augment a collaboration graph with latent links between collaborators. This is combined with the solution of Steiner tree problems to form small teams that cover a specified set of tasks. Our framework not only considers the size of the team but also the likelihood that team members are going to collaborate with each other. We demonstrate our results using data from the US Patent office covering two different companies’ inventor networks. The results show that this technique can reduce the size of suggested teams.

    Item Type: Article
    Keywords: Team formation; Link prediction; Steiner tree;
    Academic Unit: Faculty of Science and Engineering > Mathematics and Statistics
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 15353
    Identification Number: https://doi.org/10.1007/s41109-020-00306-x
    Depositing User: Dr. David Malone
    Date Deposited: 31 Jan 2022 10:36
    Journal or Publication Title: Applied Network Science
    Publisher: SpringerOpen
    Refereed: Yes
    URI:

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