Overlapping communities on social networks : a self-falsifiable hierarchical detection algorithm

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Sloan School of Management

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Li, Tianyi,Ph.D.Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• Hazhir Rahmandad and John Sterman.

• Sloan School of Management.

Statement 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/126965

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/126965