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. 2019 Nov 14;9:16754. doi: 10.1038/s41598-019-53252-9

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© The Author(s) 2019

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/.

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(a) Representation of the network resulted from the final matrix Wⁿ, with all 2,455 congressmen in the database, disregarding the time factor. Each node is connected to its highest weighted neighbor, in terms of votes similarity on legislative bills. The red color denotes convicted representatives (33 in total). (b) A subgraph of the consolidated network, showed in (a), displaying only the 33 already arrested or convicted representatives (in red) and their respective highest weighted neighbors. We opted for not displaying the names of representatives who currently have not been arrested or officially convicted in this graph (in green). (c) Predictions based on the $n$ highest weighted neighbors, in terms of votes similarity, resulted in an average accuracy of 0.243 when $n = 1$ . (d) Tests made by considering the n–st highest weighted neighbor of a convicted node show that, as we increase the value of $n$ , the lower is the average accuracy.