scholarly journals A scalable community detection algorithm for large graphs using stochastic block models

2017 ◽  
Vol 21 (6) ◽  
pp. 1463-1485 ◽  
Author(s):  
Chengbin Peng ◽  
Zhihua Zhang ◽  
Ka-Chun Wong ◽  
Xiangliang Zhang ◽  
David E. Keyes
2018 ◽  
Vol 22 (1) ◽  
pp. 239
Author(s):  
Chengbin Peng ◽  
Zhihua Zhang ◽  
Ka-Chun Wong ◽  
Xiangliang Zhang ◽  
David E. Keyes

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 680
Author(s):  
Hanyang Lin ◽  
Yongzhao Zhan ◽  
Zizheng Zhao ◽  
Yuzhong Chen ◽  
Chen Dong

There is a wealth of information in real-world social networks. In addition to the topology information, the vertices or edges of a social network often have attributes, with many of the overlapping vertices belonging to several communities simultaneously. It is challenging to fully utilize the additional attribute information to detect overlapping communities. In this paper, we first propose an overlapping community detection algorithm based on an augmented attribute graph. An improved weight adjustment strategy for attributes is embedded in the algorithm to help detect overlapping communities more accurately. Second, we enhance the algorithm to automatically determine the number of communities by a node-density-based fuzzy k-medoids process. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms can effectively detect overlapping communities with fewer parameters compared to the baseline methods.


Sign in / Sign up

Export Citation Format

Share Document