Community discovery algorithm of complex network attention model

2022 ◽  
Author(s):  
Jinghong Wang ◽  
Haokang Li ◽  
Lina Liang ◽  
Yi Zhou
Keyword(s):  
Complex Network ◽  
Community Discovery ◽  
Attention Model

Parallel Multi-Label Propagation Based on Influence Model and Its Application to Overlapping Community Discovery

2017 ◽  
Vol 26 (03) ◽  
pp. 1760013 ◽  
Author(s):  
Qirong Qiu ◽  
Wenzhong Guo ◽  
Yuzhong Chen ◽  
Kun Guo ◽  
Rongrong Li
Keyword(s):  
Complex Network ◽  
Label Propagation ◽  
Community Discovery ◽  
Influence Model ◽  
Propagation Algorithm ◽  
Overlapping Community ◽  
High Scalability ◽  
Computationally Intensive ◽  
Mapreduce Model ◽  
Intensive Work

Finding communities in networks is one of the challenging issues in complex network research. We have to deal with very large networks that contain billions of vertices, which makes community discovery a computationally intensive work. Moreover, communities usually overlap each other, which greatly increases the difficulty of identifying the boundaries of communities. In this paper, we propose a parallel multi-label propagation algorithm (PMLPA) that enhances traditional multi-label propagation algorithm (MLPA) in two ways. First, the critical steps of MLPA are parallelized based on the MapReduce model to get higher scalability. Second, new label updating strategy is used to automatically determine the most valuable labels of each vertex. Furthermore, we study the improvement of PMLPA through considering the influence of vertices and labels on label updating. In this way, the importance of each label can be described with higher precision. Experiments on artificial and real networks prove that the proposed algorithms can achieve both high discovering accuracy and high scalability.

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