Identifying Influence of Nodes in Complex Networks with Coreness Centrality: Decreasing the Impact of Densely Local Connection

doi:10.1088/0256-307X/33/2/028901

Chin. Phys. Lett.

2016, Vol. 33

Issue (02): 028901 DOI: 10.1088/0256-307X/33/2/028901

CROSS-DISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY

Identifying Influence of Nodes in Complex Networks with Coreness Centrality: Decreasing the Impact of Densely Local Connection

Yi-Run Ruan^**, Song-Yang Lao, Yan-Dong Xiao, Jun-De Wang, Liang Bai

Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073

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Yi-Run Ruan, Song-Yang Lao, Yan-Dong Xiao et al 2016 Chin. Phys. Lett. 33 028901

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Abstract Ranking nodes by their spreading ability has been a fundamental issue related to many real applications, such as information and disease control. In the well-known coreness centrality measure, nodes' influence is ranked only by summing all neighbors' $k$-shell values while the effect of the topological connections among them on the nodes' spreading ability are ignored. In this work, we propose an improved coreness measure by decreasing the impact of densely local connections. Comparing the results from a series of susceptible-infected-recovered simulations on real networks, we show that our improved method can rank the nodes' spreading ability more accurately than other ranking measures such as $k$-shell, distance based method, mixed degree decomposition and coreness centrality method.

Received: 28 September 2015 Published: 26 February 2016

PACS:	89.75.Fb	(Structures and organization in complex systems)
	89.75.Hc	(Networks and genealogical trees)
	89.75.Kd	(Patterns)

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https://cpl.iphy.ac.cn/10.1088/0256-307X/33/2/028901 OR https://cpl.iphy.ac.cn/Y2016/V33/I02/028901

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	Yi-Run Ruan
	Song-Yang Lao
	Yan-Dong Xiao
	Jun-De Wang
	Liang Bai

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