A novel measure for influence nodes across complex networks based on node attraction

Bin Wang, Wanghao Guan, Yuxuan Sheng, Jinfang Sheng, Jinying Dai, Junkai Zhang, Qiong Li, Qiangqiang Dong and Long Chen
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Bin Wang: School of Computer Science and Engineering, Central South University Changsha, Hunan, P. R. China
Wanghao Guan: School of Computer Science and Engineering, Central South University Changsha, Hunan, P. R. China
Yuxuan Sheng: School of Computer Science and Engineering, Central South University Changsha, Hunan, P. R. China
Jinfang Sheng: School of Computer Science and Engineering, Central South University Changsha, Hunan, P. R. China
Jinying Dai: School of Computer Science and Engineering, Central South University Changsha, Hunan, P. R. China
Junkai Zhang: School of Computer Science and Engineering, Central South University Changsha, Hunan, P. R. China
Qiong Li: School of Computer Science and Engineering, Central South University Changsha, Hunan, P. R. China
Qiangqiang Dong: School of Computer Science and Engineering, Central South University Changsha, Hunan, P. R. China
Long Chen: School of Computer Science and Engineering, Central South University Changsha, Hunan, P. R. China

International Journal of Modern Physics C (IJMPC), 2021, vol. 32, issue 01, 1-19

Abstract: The real-world network is heterogeneous, and it is an important and challenging task to effectively identify the influential nodes in complex networks. Identification of influential nodes is widely used in social, biological, transportation, information and other networks with complex structures to help us solve a variety of complex problems. In recent years, the identification of influence nodes has received a lot of attention, and scholars have proposed various methods based on different practical problems. This paper proposes a new method to identify influential nodes, namely Attraction based on Node and Community (ANC). By considering the attraction of nodes to nodes and nodes to community structure, this method quantifies the attraction of a node, and the attraction of a node is used to represent its influence. To illustrate the effectiveness of ANC, we did extensive experiments on six real-world networks and the results show that the ANC algorithm is superior to the representative algorithms in terms of the accuracy and has lower time complexity as well.

Keywords: Complex networks; influential nodes; node attraction; community structure (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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DOI: 10.1142/S0129183121500121

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