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dc.contributor.advisorPatrick Jaillet and Troy Lau.en_US
dc.contributor.authorDonato Ridgley, Israel Louis.en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2019-07-12T17:40:35Z
dc.date.available2019-07-12T17:40:35Z
dc.date.copyright2018en_US
dc.date.issued2018en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 55-57).en_US
dc.description.abstractWhen assembling a team, it is imperative to assess the ability of the team to perform the task in question and to compare the performance of potential teams. In this thesis, I investigate the predictive power of different community detection methods in determining team performance in the self-organizing Kaggle platform and find that my methodology can achieve an average accuracy of 57% when predicting the result of a competition while using no performance information to identify communities. First, I motivate our interest in team performance and why a network setting is useful, as well as present the Kaggle platform as a collaboration network of users on teams participating in competitions. Next, in order to identify communities, I applied a selection of techniques to project the Kaggle network onto a team network and applied both spectral methods and DBSCAN to identify communities of teams while remaining ignorant of their performances. Finally, I generated cross-cluster performance distributions, evaluated the significance of communities found, and calculated a predictor statistic. Using holdout validation, I test and compare the merits of the different community detection methods and find that the Cosine Similarity in conjunction with spectral methods yields the best performance and provides an average accuracy of 57% when predicting the pairwise results of a competition.en_US
dc.description.statementofresponsibilityby Israel Louis Donato Ridgley.en_US
dc.format.extent57 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleDecoding team performance in a self-organizing collaboration network using community structureen_US
dc.typeThesisen_US
dc.description.degreeM. Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.identifier.oclc1098171900en_US
dc.description.collectionM.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienceen_US
dspace.imported2019-07-17T21:02:27Zen_US


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