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. 2018 Aug 29;8:12997. doi: 10.1038/s41598-018-31202-1

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© The Author(s) 2018

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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Creation of consensus WSBM model. (a) A representative adjacency matrix was constructed by averaging across multiple individual matrices. This averaging was performed with specific restraints to mitigate bias. (b) The number of blocks must be specified a priori; to identify an appropriate number of blocks (k), the WSBM was fit 100 times at k = 6, 7…11. We recorded the average log-evidence of 100 model fits at each k and used Bayes factor to determine k in a data-driven manner. (Note that the red box indicates the k = 10 parameter identified in this study) (c) At the k with the highest likelihood, for each of the 100 fits, we recorded the community assignment for each node as a vector. We then computed the least distant community assignment vector from the 100 fits. (d) Aligned results of the 100 fits were averaged to create a community assignment prior. This process was repeated until a convergence criterion was met.