Identifying multiple influential spreaders based on generalized closeness centrality

Huan-Li Liu, Chuang Ma, Bing-Bing Xiang, Ming Tang and Hai-Feng Zhang

Physica A: Statistical Mechanics and its Applications, 2018, vol. 492, issue C, 2237-2248

Abstract: To maximize the spreading influence of multiple spreaders in complex networks, one important fact cannot be ignored: the multiple spreaders should be dispersively distributed in networks, which can effectively reduce the redundance of information spreading. For this purpose, we define a generalized closeness centrality (GCC) index by generalizing the closeness centrality index to a set of nodes. The problem converts to how to identify multiple spreaders such that an objective function has the minimal value. By comparing with the K-means clustering algorithm, we find that the optimization problem is very similar to the problem of minimizing the objective function in the K-means method. Therefore, how to find multiple nodes with the highest GCC value can be approximately solved by the K-means method. Two typical transmission dynamics—epidemic spreading process and rumor spreading process are implemented in real networks to verify the good performance of our proposed method.

Keywords: Complex networks; Multiple influential spreaders; Generalized closeness centrality; K-means method (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:492:y:2018:i:c:p:2237-2248

DOI: 10.1016/j.physa.2017.11.138

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