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Patrick Jaillet and Troy Lau. Donato Ridgley, Israel Louis. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2019-07-12T17:40:35Z 2019-07-12T17:40:35Z 2018 2018 This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018 Cataloged from PDF version of thesis. Includes bibliographical references (pages 55-57). When 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. by Israel Louis Donato Ridgley. M.Eng. M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 57 pages eng Massachusetts Institute of Technology MIT 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. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Decoding team performance in a self-organizing collaboration network using community structure Thesis 1098171900 application/pdf 2019-07-17T21:02:27Z Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="5b9e37bb-347f-4601-a60b-bae0e1135b7d"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Decoding team performance in a self-organizing collaboration network using community structure</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2018</PublicationDate> <Authors> <Author> <DisplayName>Donato Ridgley, Israel Louis.</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>When 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.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>