A complex network community detection algorithm based on label propagation and fuzzy C-means

2019 ◽  
Vol 519 ◽  
pp. 217-226 ◽  
Author(s):  
Zheng-Hong Deng ◽  
Hong-Hai Qiao ◽  
Qun Song ◽  
Li Gao
Keyword(s):  
Complex Network ◽  
Community Detection ◽  
Detection Algorithm ◽  
Label Propagation ◽  
Fuzzy C Means ◽  
Network Community ◽  
Community Detection Algorithm

scholarly journals NCSS: an effective and efficient complex network community detection algorithm

2016 ◽  
Vol 46 (4) ◽  
pp. 431-444
Author(s):  
Zhongming HAN ◽  
Xusheng TAN ◽  
Yan CHEN ◽  
Dagao DUAN
Keyword(s):  
Complex Network ◽  
Community Detection ◽  
Detection Algorithm ◽  
Network Community ◽  
Community Detection Algorithm

Label Propagation Community Detection Algorithm Based on Density Peak Optimization

2021 ◽  
Author(s):  
Yan Ma ◽  
Guoqiang Chen
Keyword(s):  
Complex Network ◽  
Community Detection ◽  
Monitoring Network ◽  
Simple Algorithm ◽  
Detection Algorithm ◽  
Label Propagation ◽  
Density Peak ◽  
Propagation Algorithm ◽  
Clustering Center ◽  
Community Detection Algorithm

Abstract Community structure detection in complex network structure and function to understand network relations, found its evolution rule, monitoring and forecasting its evolution behavior has important theoretical significance, in the epidemic monitoring, network public opinion analysis, recommendation, advertising push and combat terrorism and safeguard national security has wide application prospect. Label propagation algorithm is one of the popular algorithms for community detection in recent years, the community detection algorithm based on tags spread the biggest advantage is the simple algorithm logic, relative to the module of optimization algorithm convergence speed is very fast, the clustering process without any optimization function, and the initialization before do not need to specify the number of complex network community. However, the algorithm has some problems such as unstable partitioning results and strong randomness. To solve this problem, this paper proposes an unsupervised label propagation community detection algorithm based on density peak. The proposed algorithm first introduces the density peak to find the clustering center, first determines the prototype of the community, and then fixes the number of communities and the clustering center of the complex network, and then uses the label propagation algorithm to detect the community, which improves the accuracy and robustness of community discovery, reduces the number of iterations, and accelerates the formation of the community. Finally, experiments on synthetic network and real network data sets are carried out with the proposed algorithm, and the results show that the proposed method has better performance.

Air quality analysis of Sichuan province based on complex network and CSP algorithm

2021 ◽  
pp. 2250007
Author(s):  
Xiao Li Huang ◽  
Si Yu Hu ◽  
Jing Xian Chen ◽  
Wan Qi Feng
Keyword(s):  
Air Quality ◽  
Complex Network ◽  
Community Detection ◽  
Network Theory ◽  
Strong Correlation ◽  
Cross Correlation ◽  
Detection Algorithm ◽  
Sichuan Province ◽  
Complex Network Theory ◽  
Community Detection Algorithm

The air quality is directly related to people’s lives. This paper selects air quality data of Sichuan Province as the research object, and explores the inherent characteristics of air quality from the perspective of complex network theory. First, based on the complexity of network topology and nodes, a community detection algorithm which combines the clustering idea with principal component analysis (PCA) algorithm and self-organization competitive neural network (SOM) is designed (CSP). Compared with the classic community detection algorithm, the result proves that the CSP algorithm can accurately dig out a better community structure. Second, based on the strong correlation distance and strong correlation coefficient of the air quality network, the Sichuan Air Quality Complex Network (SCCN) was constructed. The SCCN is divided into five communities using the CSP algorithm. Combining the characteristics of each community and the Hurst coefficient, it is found that the air quality inside the community has long-term memory. Finally, based on the idea of time-dependent cross-correlation, this paper analyzes the cross-correlation of AQI time series of different stations in each community, constructs a directed air quality cross-correlation network combined with complex network theory, and locates the important pollution sources in each region of Sichuan Province according to the topological structure of the network. The work of this paper can provide the corresponding theoretical support and guidance for the current environmental pollution control.

Community detection in complex network by network embedding and density clustering

2021 ◽  
pp. 1-12
Author(s):  
JinFang Sheng ◽  
Huaiyu Zuo ◽  
Bin Wang ◽  
Qiong Li
Keyword(s):  
Complex Network ◽  
Community Detection ◽  
Dimensional Space ◽  
Detection Algorithm ◽  
Superior Performance ◽  
Network Embedding ◽  
Detection Algorithms ◽  
Density Clustering ◽  
Community Detection Algorithm ◽  
Low Dimensional

 In a complex network system, the structure of the network is an extremely important element for the analysis of the system, and the study of community detection algorithms is key to exploring the structure of the complex network. Traditional community detection algorithms would represent the network using an adjacency matrix based on observations, which may contain redundant information or noise that interferes with the detection results. In this paper, we propose a community detection algorithm based on density clustering. In order to improve the performance of density clustering, we consider an algorithmic framework for learning the continuous representation of network nodes in a low-dimensional space. The network structure is effectively preserved through network embedding, and density clustering is applied in the embedded low-dimensional space to compute the similarity of nodes in the network, which in turn reveals the implied structure in a given network. Experiments show that the algorithm has superior performance compared to other advanced community detection algorithms for real-world networks in multiple domains as well as synthetic networks, especially when the network data chaos is high.

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