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dc.contributor.advisorHazhir Rahmandad and John Sterman.en_US
dc.contributor.authorLi, Tianyi,S.M.Massachusetts Institute of Technology.en_US
dc.contributor.otherSloan School of Management.en_US
dc.date.accessioned2020-09-03T16:45:43Z
dc.date.available2020-09-03T16:45:43Z
dc.date.copyright2020en_US
dc.date.issued2020en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/126965
dc.descriptionThesis: S.M. in Management Research, Massachusetts Institute of Technology, Sloan School of Management, May, 2020en_US
dc.descriptionCataloged from the official PDF of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 37-41).en_US
dc.description.abstractCommunity detection is a central topic in network studies, whereas no community detection algorithm can be optimal for all possible networks; thus it is important to identify whether the algorithm is suitable for a given network. We propose a multi-step algorithmic solution scheme for overlapping community detection based on an advanced label propagation process, which imitates the community formation process on social networks. Our algorithm is parameter-free and is able to reveal the hierarchical order of communities in the graph. The unique property of our solution scheme is self-falsifiability; an automatic quality check of the results is conducted after the detection, and the fitness of the algorithm for the specific network is reported. Extensive experiments show that our algorithm is self-consistent, reliable on networks of a wide range of size and different sorts, and is more robust than existing algorithms on both sparse and large-scale social networks. Results further suggest that our solution scheme may uncover features of networks' intrinsic community structures, which implies that this study builds up potential theoretical ground for future research, beyond expected applications in a wider-scale.en_US
dc.description.statementofresponsibilityby Tianyi Li.en_US
dc.format.extent41 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectSloan School of Management.en_US
dc.titleOverlapping communities on social networks : a self-falsifiable hierarchical detection algorithmen_US
dc.typeThesisen_US
dc.description.degreeS.M. in Management Researchen_US
dc.contributor.departmentSloan School of Managementen_US
dc.identifier.oclc1191221606en_US
dc.description.collectionS.M.inManagementResearch Massachusetts Institute of Technology, Sloan School of Managementen_US
dspace.imported2020-09-03T16:45:43Zen_US
mit.thesis.degreeMasteren_US
mit.thesis.departmentSloanen_US


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