scholarly journals Detecting overlapping community structure: Estonian network of payments

2019 ◽  
Vol 68 (1) ◽  
pp. 79
Author(s):  
S R de la Torre ◽  
J Kalda ◽  
R Kitt ◽  
J Engelbrecht
2017 ◽  
Vol 31 (15) ◽  
pp. 1750121 ◽  
Author(s):  
Fang Hu ◽  
Youze Zhu ◽  
Yuan Shi ◽  
Jianchao Cai ◽  
Luogeng Chen ◽  
...  

In this paper, based on Walktrap algorithm with the idea of random walk, and by selecting the neighbor communities, introducing improved signed probabilistic mixture (SPM) model and considering the edges within the community as positive links and the edges between the communities as negative links, a novel algorithm Walktrap-SPM for detecting overlapping community is proposed. This algorithm not only can identify the overlapping communities, but also can greatly increase the objectivity and accuracy of the results. In order to verify the accuracy, the performance of this algorithm is tested on several representative real-world networks and a set of computer-generated networks based on LFR benchmark. The experimental results indicate that this algorithm can identify the communities accurately, and it is more suitable for overlapping community detection. Compared with Walktrap, SPM and LMF algorithms, the presented algorithm can acquire higher values of modularity and NMI. Moreover, this new algorithm has faster running time than SPM and LMF algorithms.


PLoS ONE ◽  
2011 ◽  
Vol 6 (5) ◽  
pp. e19608 ◽  
Author(s):  
Kai Wu ◽  
Yasuyuki Taki ◽  
Kazunori Sato ◽  
Yuko Sassa ◽  
Kentaro Inoue ◽  
...  

2019 ◽  
Vol 38 (5) ◽  
pp. 1091-1110
Author(s):  
Yan Xing ◽  
Fanrong Meng ◽  
Yong Zhou ◽  
Guibin Sun ◽  
Zhixiao Wang

Author(s):  
Zakariya Ghalmane ◽  
Mohammed El Hassouni ◽  
Hocine Cherifi

2012 ◽  
Vol 29 ◽  
pp. 4244-4248 ◽  
Author(s):  
Cui Li

2014 ◽  
Vol 644-650 ◽  
pp. 3295-3299
Author(s):  
Lin Li ◽  
Zheng Min Xia ◽  
Sheng Hong Li ◽  
Li Pan ◽  
Zhi Hua Huang

Community structure is an important feature to understand structural and functional properties in various complex networks. In this paper, we use Multidimensional Scaling (MDS) to map nodes of network into Euclidean space to keep the distance information of nodes, and then we use topology feature of communities to propose the local expansion strategy to detect initial seeds for FCM. Finally, the FCM are used to uncover overlapping communities in the complex networks. The test results in real-world and artificial networks show that the proposed algorithm is efficient and robust in uncovering overlapping community structure.


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