Overlapping Community Detection Using Core Label Propagation and Belonging Function

2016 ◽  
pp. 165-174 ◽  
Author(s):  
Jean-Philippe Attal ◽  
Maria Malek ◽  
Marc Zolghadri
Keyword(s):  
Community Detection ◽  
Label Propagation ◽  
Overlapping Community Detection ◽  
Overlapping Community

Balanced Multi-Label Propagation for Overlapping Community Detection in Social Networks

2012 ◽  
Vol 27 (3) ◽  
pp. 468-479 ◽  
Author(s):  
Zhi-Hao Wu ◽  
You-Fang Lin ◽  
Steve Gregory ◽  
Huai-Yu Wan ◽  
Sheng-Feng Tian
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Label Propagation ◽  
Overlapping Community Detection ◽  
Overlapping Community

scholarly journals Overlapping Community Detection Based on Membership Degree Propagation

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 15
Author(s):  
Rui Gao ◽  
Shoufeng Li ◽  
Xiaohu Shi ◽  
Yanchun Liang ◽  
Dong Xu
Keyword(s):  
Complex Network ◽  
Community Detection ◽  
Real World ◽  
Detection Algorithm ◽  
Label Propagation ◽  
Computational Time ◽  
Membership Degree ◽  
Overlapping Community Detection ◽  
Overlapping Community ◽  
Real World Datasets

A community in a complex network refers to a group of nodes that are densely connected internally but with only sparse connections to the outside. Overlapping community structures are ubiquitous in real-world networks, where each node belongs to at least one community. Therefore, overlapping community detection is an important topic in complex network research. This paper proposes an overlapping community detection algorithm based on membership degree propagation that is driven by both global and local information of the node community. In the method, we introduce a concept of membership degree, which not only stores the label information, but also the degrees of the node belonging to the labels. Then the conventional label propagation process could be extended to membership degree propagation, with the results mapped directly to the overlapping community division. Therefore, it obtains the partition result and overlapping node identification simultaneously and greatly reduces the computational time. The proposed algorithm was applied to a synthetic Lancichinetti–Fortunato–Radicchi (LFR) dataset and nine real-world datasets and compared with other up-to-date algorithms. The experimental results show that our proposed algorithm is effective and outperforms the comparison methods on most datasets. Our proposed method significantly improved the accuracy and speed of the overlapping node prediction. It can also substantially alleviate the computational complexity of community structure detection in general.

LinkLPA: A Link-Based Label Propagation Algorithm for Overlapping Community Detection in Networks

2016 ◽  
Vol 33 (2) ◽  
pp. 308-331 ◽  
Author(s):  
Heli Sun ◽  
Jiao Liu ◽  
Jianbin Huang ◽  
Guangtao Wang ◽  
Xiaolin Jia ◽  
...  
Keyword(s):  
Community Detection ◽  
Label Propagation ◽  
Overlapping Community Detection ◽  
Propagation Algorithm ◽  
Overlapping Community
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