Identifying influential spreaders by gravity model

Li, Z, Ren, T, Ma, X ORCID: https://orcid.org/0000-0003-0074-4192, Liu, S, Zhang, Y and Zhou, T, 2019. Identifying influential spreaders by gravity model. Scientific Reports, 9: 8387. ISSN 2045-2322

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Abstract

Identifying influential spreaders in complex networks is crucial in understanding, controlling and accelerating spreading processes for diseases, information, innovations, behaviors, and so on. Inspired by the gravity law, we propose a gravity model that utilizes both neighborhood information and path information to measure a node’s importance in spreading dynamics. In order to reduce the accumulated errors caused by interactions at distance and to lower the computational complexity, a local version of the gravity model is further proposed by introducing a truncation radius. Empirical analyses of the Susceptible-Infected-Recovered (SIR) spreading dynamics on fourteen real networks show that the gravity model and the local gravity model perform very competitively in comparison with well-known state-of-the-art methods. For the local gravity model, the empirical results suggest an approximately linear relation between the optimal truncation radius and the average distance of the network.

Item Type:	Journal article
Publication Title:	Scientific Reports
Creators:	Li, Z., Ren, T., Ma, X., Liu, S., Zhang, Y. and Zhou, T.
Publisher:	Springer Nature Publishing AG
Date:	10 June 2019
Volume:	9
ISSN:	2045-2322
Identifiers:	Number Type 10.1038/s41598-019-44930-9 DOI s41598-019-44930-9 Publisher Item Identifier
Rights:	Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. © The Author(s) 2019.
Divisions:	Schools > School of Science and Technology
Record created by:	Jill Tomkinson
Date Added:	01 Jul 2019 14:53
Last Modified:	01 Jul 2019 14:53
URI:	https://irep.ntu.ac.uk/id/eprint/36987

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