Characterizing the fuzzy community structure in link graph via the likelihood optimization

Li, Hui-Jia; Song, Shenpeng; Tan, Wenze; Huang, Zhaoci; Li, Xiaoyan; Xu, Wenzhe; Cao, Jie

doi:10.1016/j.neucom.2022.09.013

Cited by 15 publications

(1 citation statement)

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“…The article provides an overview of representative community detection techniques, identifies the detection limitations encountered in community discovery, and affirms the data processing capabilities of representation learning. The team led by Hui-Jia et al (2022) has developed metrics and models to assess network structure, allowing for a quantitative evaluation of structural exploration. Xu et al (2021) found that the Kuramoto oscillation model’s synchronization algorithm could be employed to reveal the overlapping structural features of a network.…”

Section: Introductionmentioning

confidence: 99%

Adaptive robust structure exploration for complex systems based on model configuration and fusion

Qu,

Liu,

Wen

et al. 2024

PeerJ Computer Science

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Analyzing and obtaining useful information is challenging when facing a new complex system. Traditional methods often focus on specific structural aspects, such as communities, which may overlook the important features and result in biased conclusions. To address this, this article suggests an adaptive algorithm for exploring complex system structures using a generative model. This method calculates and optimizes node parameters, which can reflect the latent structural characteristics of the complex system. The effectiveness and stability of this method have been demonstrated in comparative experiments on 10 sets of benchmark networks using our model parameter configuration scheme. To enhance adaptability, algorithm fusion strategies were also proposed and tested on two real-world networks. The results indicate that the algorithm can uncover multiple structural features, including clustering, overlapping, and local chaining. This adaptive algorithm provides a promising approach for exploring complex system structures.

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Section: Introductionmentioning

confidence: 99%