LPA-CBD an improved label propagation algorithm based on community belonging degree for community detection

Chun Gui (), Ruisheng Zhang, Zhili Zhao (), Jiaxuan Wei () and Rongjing Hu ()
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Chun Gui: School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, P. R. China
Ruisheng Zhang: School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, P. R. China
Zhili Zhao: School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, P. R. China
Jiaxuan Wei: School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, P. R. China
Rongjing Hu: School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, P. R. China

International Journal of Modern Physics C (IJMPC), 2018, vol. 29, issue 02, 1-13

Abstract: In order to deal with stochasticity in center node selection and instability in community detection of label propagation algorithm, this paper proposes an improved label propagation algorithm named label propagation algorithm based on community belonging degree (LPA-CBD) that employs community belonging degree to determine the number and the center of community. The general process of LPA-CBD is that the initial community is identified by the nodes with the maximum degree, and then it is optimized or expanded by community belonging degree. After getting the rough structure of network community, the remaining nodes are labeled by using label propagation algorithm. The experimental results on 10 real-world networks and three synthetic networks show that LPA-CBD achieves reasonable community number, better algorithm accuracy and higher modularity compared with other four prominent algorithms. Moreover, the proposed algorithm not only has lower algorithm complexity and higher community detection quality, but also improves the stability of the original label propagation algorithm.

Keywords: Community detection; randomness; belonging degree; label propagation; modularity (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0129183118500110

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