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. 2021 Apr 28;11:9190. doi: 10.1038/s41598-021-88692-9

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© The Author(s) 2021

Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

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The average degree of the identified node set by different approaches on four datasets. The average degree of nodes identified with JN is always greater than that with FN or SP in each fraction and each dataset. With the increasing of the fraction, the gap between JN and FN shrinks, because there are not enough high degree nodes left in the network. This implies that JN can find high degree nodes more efficiently than FN. The results are averaged over 50 independent runs.