dc.contributor.advisor | Hazhir Rahmandad and John Sterman. | en_US |
dc.contributor.author | Li, Tianyi,Ph.D.Massachusetts Institute of Technology. | en_US |
dc.contributor.other | Sloan School of Management. | en_US |
dc.date.accessioned | 2020-09-03T16:45:43Z | |
dc.date.available | 2020-09-03T16:45:43Z | |
dc.date.copyright | 2020 | en_US |
dc.date.issued | 2020 | en_US |
dc.identifier.uri | https://hdl.handle.net/1721.1/126965 | |
dc.description | Thesis: S.M. in Management Research, Massachusetts Institute of Technology, Sloan School of Management, May, 2020 | en_US |
dc.description | Cataloged from the official PDF of thesis. | en_US |
dc.description | Includes bibliographical references (pages 37-41). | en_US |
dc.description.abstract | Community 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.statementofresponsibility | by Tianyi Li. | en_US |
dc.format.extent | 41 pages | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Massachusetts Institute of Technology | en_US |
dc.rights | MIT 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.uri | http://dspace.mit.edu/handle/1721.1/7582 | en_US |
dc.subject | Sloan School of Management. | en_US |
dc.title | Overlapping communities on social networks : a self-falsifiable hierarchical detection algorithm | en_US |
dc.type | Thesis | en_US |
dc.description.degree | S.M. in Management Research | en_US |
dc.contributor.department | Sloan School of Management | en_US |
dc.identifier.oclc | 1191221606 | en_US |
dc.description.collection | S.M.inManagementResearch Massachusetts Institute of Technology, Sloan School of Management | en_US |
dspace.imported | 2020-09-03T16:45:43Z | en_US |
mit.thesis.degree | Master | en_US |
mit.thesis.department | Sloan | en_US |