An efficient graph clustering algorithm in signed graph based on modularity maximization

Kefan Zhuo, Zhuoxuan Yang, Guan Yan, Kai Yu and Wenqiang Guo
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Kefan Zhuo: School of Management Science and Engineering, Central University of Finance and Economics, Beijing 100081, P. R. China
Zhuoxuan Yang: School of Economics and Management, North China Electric Power University, Beijing 102206, P. R. China
Guan Yan: School of Management Science and Engineering, Central University of Finance and Economics, Beijing 100081, P. R. China
Kai Yu: School of Computer Science and Engineering, Xinjiang University of Finance and Economics, Urumqi, Xinjiang 830012, P. R. China
Wenqiang Guo: School of Computer Science and Engineering, Xinjiang University of Finance and Economics, Urumqi, Xinjiang 830012, P. R. China

International Journal of Modern Physics C (IJMPC), 2019, vol. 30, issue 11, 1-15

Abstract: The unsigned graphs containing positive links only, have been analyzed fruitfully. However, the physical relations behind complex networks are dissimilar. We often encounter the signed networks that have both positive and negative links as well. It is very important to study the characteristics of complex networks and predict individual attitudes by analyzing the attitudes of individuals and their neighbors, which can divide individuals into different clusters or communities. To detect the clusters in signed networks, first, a modularity function for signed networks is proposed on the basis of the combination of positive and negative part. Then, a new graph clustering algorithm for signed graphs has also been proposed based on CNM algorithm, which has high efficiency. Finally, the algorithm has been applied on both artificial and the real networks. The results show that the proposed method has been able to achieve near-perfect solution, which is suitable for multiple types real networks.

Keywords: Signed networks; cluster configuration; modularity function; optimization (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1142/S0129183119500955

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