FluidC+: A novel community detection algorithm based on fluid propagation

Jinfang Sheng (), Kai Wang (), Zejun Sun, Jie Hu (), Bin Wang and Aman Ullah ()
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Jinfang Sheng: School of Computer Science and Engineering, Central South University, Changsha, Hunan, P. R. China
Kai Wang: School of Computer Science and Engineering, Central South University, Changsha, Hunan, P. R. China
Zejun Sun: School of Computer Science and Engineering, Central South University, Changsha, Hunan, P. R. China†Department of Network Center, Pingdingshan University, Pingdingshan, Henan, P. R. China
Jie Hu: School of Computer Science and Engineering, Central South University, Changsha, Hunan, P. R. China
Bin Wang: School of Computer Science and Engineering, Central South University, Changsha, Hunan, P. R. China
Aman Ullah: School of Computer Science and Engineering, Central South University, Changsha, Hunan, P. R. China

International Journal of Modern Physics C (IJMPC), 2019, vol. 30, issue 04, 1-17

Abstract: In recent years, community detection has gradually become a hot topic in the complex network data mining field. The research of community detection is helpful not only to understand network topology structure but also to explore network hiding function. In this paper, we improve FluidC which is a novel community detection algorithm based on fluid propagation, by ameliorating the quality of seed set based on positive feedback and determining the node update order. We first summarize the shortcomings of FluidC and analyze the reasons result in these drawbacks. Then, we took some effective measures to overcome them and proposed an efficient community detection algorithm, called FluidC+. Finally, experiments on the generated network and real-world network show that our method not only greatly improves the performance of the original algorithm FluidC but also is better than many state-of-the-art algorithms, especially in the performance on real-world network with ground truth.

Keywords: Data mining; community detection; propagation; cluster; positive feedback (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1142/S0129183119500219

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