MURAL - Maynooth University Research Archive Library



    Using Machine Learning to Predict Links and Improve Steiner Tree Solutions to Team Formation Problems


    Keane, Peter and 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)

    [img]
    Preview
    Download (961kB) | Preview


    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...



    Add this article to your Mendeley library


    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: https://doi.org/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
    URI:

    Repository Staff Only(login required)

    View Item Item control page

    Downloads

    Downloads per month over past year