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. 2020 Sep 2;10:14445. doi: 10.1038/s41598-020-71483-z

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© The Author(s) 2020

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 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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Shows the effect of sequential removal of active actors in both networks based on the highest degree (green) and highest betweenness centrality (red) compared to random removal (blue). On the left side, the connected network size dropped to 0 after the removal of 25% of actors, while in the collaborative network, the removal of the most connected actors was comparable to the random removal of actors. The simulation videos of the dynamics of sequential removal of the most active actors shows the interactions in each network after each removal.