# Combining Machine Learning and Social Network Analysis to Reveal the Organizational Structures
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Hyperparameters**} \\ \hline
**Min. Activity** & **\% of the Known Nodes** & **Utility Score** & **Threshold** & **Jaccard Value** \\ \hline \multirow{8}{*}{1 month} & 90\% & page rank centrality & 1 & 0.7 \\ \cline{2-5}  & 80\% & authorities centrality & 1 & 0.7 \\ \cline{2-5}  & 70\% & authorities centrality & 1 & 0.7 \\ \cline{2-5}  & 60\% & indegree centrality & 1 & 0.8 \\ \cline{2-5}  & 50\% & the biggest clique & 1 & 0.7 \\ \cline{2-5}  & 40\% & indegree centrality & 1 & 0.7 \\ \cline{2-5}  & 30\% & indegree centrality & 1 & 0.7 \\ \cline{2-5}  & 20\% & general neighborhood variability & 1 & 0.7 \\ \cline{2-5}  & 10\% & closeness centrality & 1 & 0.7 \\ \hline \multirow{8}{*}{2 months} & 90\% & received neighborhood variability & 1 & 0.7 \\ \cline{2-5}  & 80\% & closeness centrality & 1 & 0.7 \\ \cline{2-5}  & 80\% & hubs centrality & 1 & 0.7 \\ \cline{2-5}  & 70\% & closeness centrality & 1 & 0.7 \\ \cline{2-5}  & 60\% & the biggest clique & 1 & 0.7 \\ \cline{2-5}  & 50\% & the biggest clique & 1 & 0.7 \\ \cline{2-5}  & 40\% & indegree centrality & 1 & 0.7 \\ \cline{2-5}  & 30\% & indegree centrality & 1 & 0.7 \\ \cline{2-5}  & 20\% & general neighborhood variability & 1 & 0.99 \\ \cline{2-5}  & 10\% & general neighborhood variability & 1 & 0.7 \\ \hline \multirow{8}{*}{3 months} & 90\% & hubs centrality & 1 & 0.7 \\ \cline{2-5}  & 80\% & authorities centrality & 1 & 0.7 \\ \cline{2-5}  & 70\% & authorities centrality & 1 & 0.7 \\ \cline{2-5}  & 60\% & the biggest clique & 1 & 0.99 \\ \cline{2-5}  & 50\% & the biggest clique & 1 & 0.7 \\ \cline{2-5}  & 40\% & indegree centrality & 1 & 0.7 \\ \cline{2-5}  & 30\% & indegree centrality & 1 & 0.7 \\ \cline{2-5}  & 20\% & received neighborhood variability & 1 & 0.7 \\ \cline{2-5}  & 10\% & general neighborhood variability & 1 & 0.7 \\ \hline \multirow{8}{*}{4 months} & 90\% & closeness centrality & 1 & 0.7 \\ \cline{2-5}  & 80\% & indegree centrality & 1 & 0.7 \\ \cline{2-5}  & 70\% & indegree centrality & 1 & 0.99 \\ \cline{2-5}  & 60\% & the biggest clique & 1 & 0.7 \\ \cline{2-5}  & 90\% & closeness centrality & 1 & 0.7 \\ \cline{2-5}  & 80\% & indegree centrality & 1 & 0.7 \\ \cline{2-5}  & 70\% & indegree centrality & 1 & 0.99 \\ \cline{2-5}  & 60\% & the biggest clique & 1 & 0.8 \\ \cline{2-5}  & 50\% & betweenness centrality & 1 & 0.7 \\ \cline{2-5}  & 40\% & betweenness centrality & 1 & 0.7 \\ \cline{2-5}  & 30\% & page rank centrality & 1 & 0.7 \\ \cline{2-5}  & 20\% & general neighborhood variability & 1 & 0.99 \\ \cline{2-5}  & 10\% & eigenvector centrality & 1 & 0.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The best values of the hyperparameters for the collective classification algorithm for the Enron dataset with two hierarchy levels. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Hyperparameters**} \\ \hline
**Min. Activity** & **\% of the Known Nodes** & **Utility Score** & **Threshold** & **Jaccard Value** \\ \hline  & 90\% & authorities centrality & 1 & 0.7 \\  & & hubs centrality & 1 & 0.7 \\ \cline{2-5}  & 80\% & indegree centrality & 1 & 0.7 \\ \cline{2-5}  & 70\% & outdegree centrality & 1 & 0.7 \\ \cline{2-5}
5 months & 60\% & outdegree centrality & 1 & 0.7 \\ \cline{2-5}  & 50\% & betweenness centrality & 1 & 0.7 \\ \cline{2-5}  & 40\% & betweenness centrality & 1 & 0.7 \\ \cline{2-5}  & 30\% & betweenness centrality & 1 & 0.7 \\ \cline{2-5}  & 20\% & betweenness centrality & 1 & 0.7 \\ \cline{2-5}  & 10\% & the biggest clique & 1 & 0.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: _Cont.__Appl. Sci._**2020**, _10_, 1699 Figure 14: Features importance (Gini importance) for the Decision Tree which uses the full set of features for the manufacturing company dataset with two levels of the hierarchy. Figure A4. Features importance (Gini importance) for the Random Forest which uses the full set of features for the manufacturing company dataset with three levels of the hierarchy.