Community Detection in Complex Networks Using Nonnegative Matrix Factorization and Density-Based Clustering Algorithm

Lu, Hong; Zhao, Qinghua; Sang, Xiaoshuang; Lu, Jianfeng

doi:10.1007/s11063-019-10170-1

Cited by 15 publications

(6 citation statements)

References 46 publications

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“…Therefore, DP algorithm cannot be directly used to detect community networks. In order to solve this problem, this paper uses the improved DP algorithm [29] to obtain the number of communities in a complex network as the input parameter of the label propagation algorithm.…”

Section: Methodsmentioning

confidence: 99%

Label Propagation Community Detection Algorithm Based on Density Peak Optimization

Chen

2022

Wireless Communications and Mobile Computing

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Community structure detection in a complex network structure and function is used to understand network relations and find its evolution rule; monitoring and forecasting its evolution behavior have an important theoretical significance; in the epidemic monitoring, network public opinion analysis, recommendation, advertising push and combat terrorism, and safeguard national security, it has wide application prospect. A label propagation algorithm is one of the popular algorithms for community detection in recent years; the community detection algorithm based on tags that 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.

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Section: Methodsmentioning

confidence: 99%

Label Propagation Community Detection Algorithm Based on Density Peak Optimization

Chen

2022

Wireless Communications and Mobile Computing

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“…Therefore, DP algorithm cannot be directly used to detect community networks. In order to solve this problem, this paper uses the improved DP algorithm [24] to obtain the number of communities in a complex network as the input parameter of the label propagation algorithm.…”

Section: Methodsmentioning

confidence: 99%

Label Propagation Community Detection Algorithm Based on Density Peak Optimization

Chen

2021

Preprint

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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.

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“…However, the edges in the intercommunity are denser than those in the intra-community edges. Thus, identifying community structures without prior knowledge about the number of communities is difficult [31]. Furthermore, quantifying the strength of social ties and identifying the strong relationships between features and neighbor nodes is even more challenging [32,33].…”

Section: Community Detectionmentioning

confidence: 99%

Identifying Influential Communities Using IID for a Multilayer Networks

Suganthini¹,

Baskaran²

2023

Intelligent Automation &Amp; Soft Computing

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In online social networks (OSN), they generate several specific user activities daily, corresponding to the billions of data points shared. However, although users exhibit significant interest in social media, they are uninterested in the content, discussions, or opinions available on certain sites. Therefore, this study aims to identify influential communities and understand user behavior across networks in the information diffusion process. Social media platforms, such as Facebook and Twitter, extract data to analyze the information diffusion process, based on which they cascade information among the individuals in the network. Therefore, this study proposes an influential information diffusion model that identifies influential communities across these two social media sites. Moreover, it addresses site migration by visualizing a set of overlapping communities using hyper-edge detection. Thus, the overlapping community structure is used to identify similar communities with identical user interests. Furthermore, the community structure helps in determining the node activation and user influence from the information cascade model. Finally, the Fraction of Intra/Inter-Layer (FIL) diffusion score is used to evaluate the efficiency of the influential information diffusion model by analyzing the trending influential communities in a multilayer network. However, from the experimental result, it observes that the FIL diffusion score for the proposed method achieves better results in terms of accuracy, precision, recall and efficiency of community detection than the existing methods.

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Community Detection in Complex Networks Using Nonnegative Matrix Factorization and Density-Based Clustering Algorithm

Cited by 15 publications

References 46 publications

Label Propagation Community Detection Algorithm Based on Density Peak Optimization

Label Propagation Community Detection Algorithm Based on Density Peak Optimization

Label Propagation Community Detection Algorithm Based on Density Peak Optimization

Identifying Influential Communities Using IID for a Multilayer Networks

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