A Parallel Algorithm to Find Overlapping Community Structure in Directed and Weighted Complex Networks

Author(s):  
Jianping Zhang ◽  
Sheng Ge
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.


2012 ◽  
Vol 6-7 ◽  
pp. 985-990
Author(s):  
Yan Peng ◽  
Yan Min Li ◽  
Lan Huang ◽  
Long Ju Wu ◽  
Gui Shen Wang ◽  
...  

Community structure detection has great importance in finding the relationships of elements in complex networks. This paper presents a method of simultaneously taking into account the weak community structure definition and community subgraph density, based on the greedy strategy for community expansion. The results are compared with several previous methods on artificial networks and real world networks. And experimental results verify the feasibility and effectiveness of our approach.


2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Zakariya Ghalmane ◽  
Chantal Cherifi ◽  
Hocine Cherifi ◽  
Mohammed El Hassouni

2019 ◽  
Vol 28 (04) ◽  
pp. 1950011
Author(s):  
Rongwang Chen ◽  
Qingshou Wu ◽  
Wenzhong Guo ◽  
Kun Guo ◽  
Qinze Wang

We propose an overlapping community discovery algorithm that combines node influence and [Formula: see text]-connected neighbors for effectively detecting the overlapping community structure of complex networks. On the basis of the node influence and [Formula: see text]-connected neighbors, our method accurately detects the core node community and uses the improved similarity between the node and community to expand the core node community. Accordingly, the discovery and optimization of network overlapping communities are realized. Experiments on artificial and real-world networks demonstrate that our method significantly and consistently outperforms other comparison methods.


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