• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Neighborhood coreness algorithm for identifying a set of influential spreaders in complex networks

Vol. 11, No.6, June 30, 2017
10.3837/tiis.2017.06.010, Download Paper (Free):

Abstract

In recent years, there has been an increasing number of studies focused on identifying a set of spreaders to maximize the influence of spreading in complex networks. Although the k-core decomposition can effectively identify the single most influential spreader, selecting a group of nodes that has the largest k-core value as the seeds cannot increase the performance of the influence maximization because the propagation sphere of this group of nodes is overlapped. To overcome this limitation, we propose a neighborhood coreness cover and discount heuristic algorithm named “NCCDH” to identify a set of influential and decentralized seeds. Using this method, a node in the high-order shell with the largest neighborhood coreness and an uncovered status will be selected as the seed in each turn. In addition, the neighbors within the same shell layer of this seed will be covered, and the neighborhood coreness of the neighbors outside the shell layer will be discounted in the subsequent round. The experimental results show that with increases in the spreading probability, the NCCDH outperforms other algorithms in terms of the affected scale and spreading speed under the Susceptible-Infected-Recovered (SIR) and Susceptible-Infected (SI) models. Furthermore, this approach has a superior running time.


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Cite this article

[IEEE Style]
Xiong YANG, De-Cai HUANG and Zi-Ke ZHANG, "Neighborhood coreness algorithm for identifying a set of influential spreaders in complex networks," KSII Transactions on Internet and Information Systems, vol. 11, no. 6, pp. 2979-2995, 2017. DOI: 10.3837/tiis.2017.06.010

[ACM Style]
YANG, X., HUANG, D., and ZHANG, Z. 2017. Neighborhood coreness algorithm for identifying a set of influential spreaders in complex networks. KSII Transactions on Internet and Information Systems, 11, 6, (2017), 2979-2995. DOI: 10.3837/tiis.2017.06.010