Identifying influential spreaders in complex networks by an improved gravity model

Abstract Identification of influential spreaders is still a challenging issue in network science. Therefore, it attracts increasing attention from both computer science and physical societies, and many algorithms to identify influential spreaders have been proposed so far. Degree centrality, as the...

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Autores principales:	Zhe Li, Xinyu Huang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/97e9854fb53849d982f31d33d81bc976
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id	oai:doaj.org-article:97e9854fb53849d982f31d33d81bc976
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spelling	oai:doaj.org-article:97e9854fb53849d982f31d33d81bc9762021-11-14T12:19:18ZIdentifying influential spreaders in complex networks by an improved gravity model10.1038/s41598-021-01218-12045-2322https://doaj.org/article/97e9854fb53849d982f31d33d81bc9762021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01218-1https://doaj.org/toc/2045-2322Abstract Identification of influential spreaders is still a challenging issue in network science. Therefore, it attracts increasing attention from both computer science and physical societies, and many algorithms to identify influential spreaders have been proposed so far. Degree centrality, as the most widely used neighborhood-based centrality, was introduced into the network world to evaluate the spreading ability of nodes. However, degree centrality always assigns too many nodes with the same value, so it leads to the problem of resolution limitation in distinguishing the real influences of these nodes, which further affects the ranking efficiency of the algorithm. The k-shell decomposition method also faces the same problem. In order to solve the resolution limit problem, we propose a high-resolution index combining both degree centrality and the k-shell decomposition method. Furthermore, based on the proposed index and the well-known gravity law, we propose an improved gravity model to measure the importance of nodes in propagation dynamics. Experiments on ten real networks show that our model outperforms most of the state-of-the-art methods. It has a better performance in terms of ranking performance as measured by the Kendall’s rank correlation, and in terms of ranking efficiency as measured by the monotonicity value.Zhe LiXinyu HuangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zhe Li Xinyu Huang Identifying influential spreaders in complex networks by an improved gravity model
description	Abstract Identification of influential spreaders is still a challenging issue in network science. Therefore, it attracts increasing attention from both computer science and physical societies, and many algorithms to identify influential spreaders have been proposed so far. Degree centrality, as the most widely used neighborhood-based centrality, was introduced into the network world to evaluate the spreading ability of nodes. However, degree centrality always assigns too many nodes with the same value, so it leads to the problem of resolution limitation in distinguishing the real influences of these nodes, which further affects the ranking efficiency of the algorithm. The k-shell decomposition method also faces the same problem. In order to solve the resolution limit problem, we propose a high-resolution index combining both degree centrality and the k-shell decomposition method. Furthermore, based on the proposed index and the well-known gravity law, we propose an improved gravity model to measure the importance of nodes in propagation dynamics. Experiments on ten real networks show that our model outperforms most of the state-of-the-art methods. It has a better performance in terms of ranking performance as measured by the Kendall’s rank correlation, and in terms of ranking efficiency as measured by the monotonicity value.
format	article
author	Zhe Li Xinyu Huang
author_facet	Zhe Li Xinyu Huang
author_sort	Zhe Li
title	Identifying influential spreaders in complex networks by an improved gravity model
title_short	Identifying influential spreaders in complex networks by an improved gravity model
title_full	Identifying influential spreaders in complex networks by an improved gravity model
title_fullStr	Identifying influential spreaders in complex networks by an improved gravity model
title_full_unstemmed	Identifying influential spreaders in complex networks by an improved gravity model
title_sort	identifying influential spreaders in complex networks by an improved gravity model
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/97e9854fb53849d982f31d33d81bc976
work_keys_str_mv	AT zheli identifyinginfluentialspreadersincomplexnetworksbyanimprovedgravitymodel AT xinyuhuang identifyinginfluentialspreadersincomplexnetworksbyanimprovedgravitymodel
_version_	1718429303226499072

Identifying influential spreaders in complex networks by an improved gravity model

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