2026-09-19T15:37:38Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1269652022-06-23T15:23:14Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Hazhir Rahmandad and John Sterman.
Li, Tianyi,Ph.D.Massachusetts Institute of Technology.
Sloan School of Management.
Sloan School of Management
2020-09-03T16:45:43Z
2020-09-03T16:45:43Z
2020
2020
https://hdl.handle.net/1721.1/126965
1191221606
Thesis: S.M. in Management Research, Massachusetts Institute of Technology, Sloan School of Management, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 37-41).
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.
by Tianyi Li.
S.M. in Management Research
S.M.inManagementResearch Massachusetts Institute of Technology, Sloan School of Management
41 pages
eng
Massachusetts Institute of Technology
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.
http://dspace.mit.edu/handle/1721.1/7582
Sloan School of Management.
Overlapping communities on social networks : a self-falsifiable hierarchical detection algorithm
Thesis
application/pdf
2020-09-03T16:45:43Z
Publication
Master
Sloan
unknown
unknown
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<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.</Abstract>
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