A Comparative Study on Community Detection Methods in Complex Networks
Community detection aims to discover cohesive groups in which people connect with each other closely in social networks. A variety of methods have been proposed to detect communities in social networks. However, there is still few work to make a comparative study on those methods. In this paper, we first introduce and compare several representative methods on community detection. Then we implement those methods with python and make a comparative analysis on different real world social networking data sets. The experimental results have shown that GN algorithm is suitable for small networks, while LPA algorithm has a better scalability. FU algorithm is of the best stability. This work could help researchers to understand the ideas of community detection methods better and select appropriate method on demand more easily.
Citation : Zhao, Z.., Zheng, S., Sun, J., Chang, L., Chiclana, F. (2018) A Comparative Study on Community Detection Methods in Complex Networks. Journal of Intelligent and Fuzzy Systems,
ISSN : 1064-1246
Research Group : Centre for Computational Intelligence
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes