An Overlapping Community Discovery Algorithm Based on Label Propagation

2019 ◽  
Author(s):  
Xiujin Shi ◽  
Yue Wang ◽  
Keke Huang ◽  
Shuli Zhang
Keyword(s):  
Label Propagation ◽  
Community Discovery ◽  
Overlapping Community

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.

scholarly journals Overlapping Community Discovery Method Based on Two Expansions of Seeds

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 18
Author(s):  
Yan Li ◽  
Jing He ◽  
Youxi Wu ◽  
Rongjie Lv
Keyword(s):  
Symmetric Matrix ◽  
Fitness Function ◽  
Local Maximum ◽  
Community Discovery ◽  
Network Nodes ◽  
Overlapping Community ◽  
The Common ◽  
New Research ◽  
Discovery Method ◽  
Discovery Algorithms

The real world can be characterized as a complex network sto in symmetric matrix. Community discovery (or community detection) can effectively reveal the common features of network groups. The communities are overlapping since, in fact, one thing often belongs to multiple categories. Hence, overlapping community discovery has become a new research hotspot. Since the results of the existing community discovery algorithms are not robust enough, this paper proposes an effective algorithm, named Two Expansions of Seeds (TES). TES adopts the topological feature of network nodes to find the local maximum nodes as the seeds which are based on the gravitational degree, which makes the community discovery robust. Then, the seeds are expanded by the greedy strategy based on the fitness function, and the community cleaning strategy is employed to avoid the nodes with negative fitness so as to improve the accuracy of community discovery. After that, the gravitational degree is used to expand the communities for the second time. Thus, all nodes in the network belong to at least one community. Finally, we calculate the distance between the communities and merge similar communities to obtain a less- undant community structure. Experimental results demonstrate that our algorithm outperforms other state-of-the-art algorithms.

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
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