A Hierarchical Multi-label Propagation Algorithm for Overlapping Community Discovery in Social Networks

2014 ◽  
Author(s):  
Song Shi ◽  
Yuzhong Chen ◽  
Mingyue Fang ◽  
Wanhua Li ◽  
Shining
Keyword(s):  
Social Networks ◽  
Label Propagation ◽  
Community Discovery ◽  
Propagation Algorithm ◽  
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.

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

Community leader and transition probability based LPA

2020 ◽  
Vol 34 (27) ◽  
pp. 2050253
Author(s):  
Yu Ying Chen ◽  
Jimin Ye
Keyword(s):  
Transition Probability ◽  
Real Data ◽  
Label Propagation ◽  
Community Leader ◽  
Data Sets ◽  
Community Discovery ◽  
Normalized Mutual Information ◽  
Network Community ◽  
Propagation Algorithm ◽  
Close Relationship

Many practice problems can be transformed into complex networks, and complex network community discovery has become a hot research topic in various fields. The classic label propagation algorithm (LPA) can give community partition very quickly, but stability of the algorithm is poor due to random label propagation. To solve this problem, community leader principle is built and transition probability is introduced, a label propagation algorithm based on community leader and transition probability (CTLPA) is proposed. CTLPA selects threatened leaders and their communities according to the community leader principle, and uses the transition probability and the degree of the leader to jointly control the order for community merger, so that the threatened leader continuously devours the communities that threaten him, until a preliminary community partition is formed. To further reduce the number of community, in CTLPA, based on the characteristic of the community structure: close relationship within the community and sparse relationship outside the community, the closest communities are merged, until the final community partition is obtained. The CTLPA is compared with other five classic algorithms on LFR artificially generated networks and several real data sets. The experimental results show that CTLPA is robust in community partition, it always gives the same community partition, while the LPA will give different results from multiple independent runs. The number of community partition and the normalized mutual information (NMI) of the CTLPA are the best in most cases.

Overlapping Community Discovery Algorithm Based on Hierarchical Agglomerative Clustering

2017 ◽  
Vol 32 (03) ◽  
pp. 1850008 ◽  
Author(s):  
Hongtao Liu ◽  
Linghu Fen ◽  
Jie Jian ◽  
Long Chen
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
The Other ◽  
Node Degree ◽  
Agglomerative Clustering ◽  
Community Discovery ◽  
The Core ◽  
Degree Of Membership ◽  
Hierarchical Agglomerative Clustering ◽  
Overlapping Community

Overlapping community is a response to the real network structure in social networks and in real society in order to solve the problems such as the parameters of the existing overlapping community discovery algorithm being too large, excessive overlap and no guarantee of stability of multiple runs. In this paper, the method of calculating the node degree of membership was proposed, and an overlapping community discovery algorithm based on the local optimal expansion cohesion idea was designed. Firstly, the initial core community was constructed with the highest importance node and its neighbor nodes. Secondly, the core community was extended by node attribution degree until the termination condition of the algorithm was satisfied. Finally, the experimental results were compared with the existing algorithms. The experiments show that the result of the division by the improved algorithm has been significantly improved compared to the other algorithms, and the community structure after the division is more reasonable.

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