# Combining Machine Learning and Social Network Analysis to Reveal the Organizational Structures
## 4 Discussion
The manufacturing company email dataset alongside with corporate hierarchy has been published by the authors of this manuscript at Harvard Dataverse, see [[https://doi.org/10.7910/DVN/6Z3](https://doi.org/10.7910/DVN/6Z3) CGX]([https://doi.org/10.7910/DVN/6Z3](https://doi.org/10.7910/DVN/6Z3) CGX). The Enron dataset is available from Reference [1]. Conceptualization, R.M. and M.N. ; methodology, R.M. and M.N. ; software, M.N. ; validation, R.M. ; investigation, R.M. and M.N. ; data curation, R.M. and M.N. ; writing--original draft preparation, M.N. and R.M. ; writing--review and editing, R.M. ; visualization, M.N. ; supervision, R.M. ; project administration, R.M. ; funding acquisition, R.M. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Science Centre, Poland, project no. 2015/17/D/ST6/04046 as well as by the European Unions Horizon 2020 research and innovation programme under grant agreement no. 691152 (RENOIR) and the Polish Ministry of Science and Higher Education fund for supporting internationally co-financed projects in 2016-2019 (agreement no. 3628/H2020/2016/2). Authors of this manuscript would like to thank to [PERSON] for his valuable remarks. Moreover, we would like to express our gratitude to the Reviewers of the manuscript for their precious feedback. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} SNA & Social Network Analysis \\ IID & Independent and Identical Distribution \\ SVM & Support Vector Machine \\ L-BFGS & limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm \\ \end{tabular} \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & \multicolumn{10}{c}{**Percentage of the Used Features**} \\ \hline  & & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** & **60\%** & **70\%** & **80\%** & **90\%** & **100\%** \\ \hline \multirow{4}{*}{**F-score macro**} & 1 month & 0.2910 & 0.2924 & 0.3685 & 0.3319 & 0.3992 & 0.3564 & 0.3835 & **0.4737** & 0.4071 & 0.4585 \\ \cline{2-11}  & 2 months & 0.2766 & 0.2950 & 0.3541 & 0.3416 & 0.3670 & 0.3550 & 0.3981 & 0.4058 & 0.4057 & **0.4390** \\ \cline{2-11}  & 3 months & 0.3425 & 0.3378 & 0.3239 & 0.3484 & 0.3404 & 0.3928 & 0.3903 & 0.3993 & **0.4136** & 0.4100 \\ \cline{2-11}  & 4 months & 0.3024 & 0.2904 & 0.3041 & 0.3413 & 0.4014 & 0.3582 & 0.3755 & 0.3923 & 0.4156 & **0.4208** \\ \cline{2-11}  & 5 months & 0.3373 & 0.4064 & 0.4089 & 0.3723 & 0.3674 & **0.4091** & 0.3790 & 0.3651 & 0.3852 & 0.3999 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: F-score macro average for the Decision Tree classification of three levels of the hierarchy in the manufacturing company dataset. The values which are bolded are the best results for a given minimum communication activity. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & \multicolumn{10}{c}{**Percentage of the Used Features**} \\ \hline  & & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** & **60\%** & **70\%** & **80\%** & **90\%** & **100\%** \\ \hline \multirow{4}{*}{**F-score macro**} & 1 month & 0.2872 & 0.3867 & 0.4075 & 0.4065 & 0.3954 & 0.3886 & 0.3780 & 0.4312 & **0.4380** & 0.4374 \\ \cline{2-11}  & 2 months & 0.3137 & 0.3742 & 0.3389 & 0.2981 & 0.3834 & **0.4575** & 0.