Detecting community structure by belonging intensity analysis of intermediate nodes

Mengjia Shen, Dong Lv () and Zhixin Ma ()
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Mengjia Shen: School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, P. R. China
Dong Lv: School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, P. R. China
Zhixin Ma: School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, P. R. China

International Journal of Modern Physics C (IJMPC), 2019, vol. 30, issue 11, 1-18

Abstract: Community structure is a common characteristic of complex networks and community detection is an important methodology to reveal the structure of real-world networks. In recent years, many algorithms have been proposed to detect the high-quality communities in real-world networks. However, these algorithms have shortcomings of performing calculation on the whole network or defining objective function and the number of commonties in advance, which affects the performance and complexity of community detection algorithms. In this paper, a novel algorithm has been proposed to detect communities in networks by belonging intensity analysis of intermediate nodes, named BIAS, which is inspired from the interactive behavior in human communication networks. More specifically, intermediate nodes are middlemen between different groups in social networks. BIAS algorithm defines belonging intensity using local interactions and metrics between nodes, and the belonging intensity of intermediate node in different communities is analyzed to distinguish which community the intermediate node belongs to. The experiments of our algorithm with other state-of-the-art algorithms on synthetic networks and real-world networks have shown that BIAS algorithm has better accuracy and can significantly improve the quality of community detection without prior information.

Keywords: Complex networks; community structure; local interactions; belonging intensity; intermediate nodes (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1142/S0129183119500797

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