# Combining Machine Learning and Social Network Analysis to Reveal the Organizational Structures
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Min. Activity**} \\ \hline  & & **1 Month** & **2 Months** & **3 Months** & **4 Months** & **5 Months** \\ \hline \multirow{2}{*}{Decision Tree} & max\_depth & 5 & 4 & 5 & 8 & 4 \\ \cline{2-6}  & max\_features & 7 & 8 & 11 & 9 & 13 \\ \hline \multirow{2}{*}{Random Forest} & max\_depth & 2 & 4 & 7 & 2 & 3 \\ \cline{2-6}  & max\_features & 2 & 12 & 2 & 14 & 14 \\ \cline{2-6}  & n\_estimators & 2 & 4 & 8 & 2 & 16 \\ \hline \multirow{2}{*}{Neural Network} & alpha & 0.001 & 0.01 & 0.01 & 0.0001 & 0.001 \\ \cline{2-6}  & hidden\_layer\_sizes & (4, 3) & (13) & (13) & (9) & (13, 9, 4) \\ \hline \multirow{2}{*}{SVM} & degree & 9 & 3 & 3 & 6 & 3 \\ \cline{2-6}  & C & 5 & 20 & 200 & 150 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: The best values of the hyperparameters for the models which uses the full set of features for the Enron dataset with two hierarchy levels. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Min. Activity**} \\ \hline  & & **1 Month** & **2 Months** & **3 Months** & **4 Months** & **5 Months** \\ \hline \multirow{2}{*}{Decision Tree} & max\_depth & 10 & 7 & 12 & 13 & 8 \\ \cline{2-6}  & max\_features & 1 & 8 & 2 & 1 & 8 \\ \hline \multirow{2}{*}{Random Forest} & max\_depth & 7 & 5 & 7 & 10 & 10 \\ \cline{2-6}  & max\_features & 3 & 11 & 14 & 6 & 7 \\ \cline{2-6}  & n\_estimators & 2 & 4 & 16 & 32 & 8 \\ \hline \multirow{2}{*}{Neural Network} & alpha & 0.001 & 0.0001 & 0.01 & 0.01 & 0.001 \\ \cline{2-6}  & hidden\_layer\_sizes & (9, 13, 4) & (4, 9, 13, 9, 4) & (4, 9, 13, 9, 4) & (13, 9, 4) \\ \hline \multirow{2}{*}{SVM} & degree & 15 & 20 & 20 & 20 & 20 \\ \cline{2-6}  & C & 200 & 200 & 200 & 200 & 150 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: The best values of the hyperparameters for the models which uses the full set of features for the Enron dataset with three hierarchy levels. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Min. Activity**} \\ \hline  & & **1 Month** & **2 Months** & **3 Months** & **4 Months** & **5 Months** \\ \hline \multirow{2}{*}{Decision Tree} & max\_depth & 10 & 7 & 12 & 13 & 8 \\ \cline{2-6}  & max\_features & 1 & 8 & 2 & 1 & 8 \\ \hline \multirow{2}{*}{Random Forest} & max\_depth & 7 & 5 & 7 & 10 & 10 \\ \cline{2-6}  & max\_features & 3 & 11 & 14 & 6 & 7 \\ \cline{2-6}  & n\_estimators & 2 & 4 & 16 & 32 & 8 \\ \hline \multirow{2}{*}{Neural Network} & alpha & 0.001 & 0.0001 & 0.01 & 0.01 & 0.001 \\ \cline{2-6}  & hidden\_layer\_sizes & (9, 13, 4) & (4, 9, 13, 9, 4) & (4, 9, 13, 9, 4) & (13, 9, 4) \\ \hline \multirow{2}{*}{SVM} & degree & 15 & 20 & 20 & 20 & 20 \\ \cline{2-6}  & C & 200 & 200 & 200 & 200 & 150 \\ \hline \hline \end{tabular}
\end{table} 
Table 18: The best values of the hyperparameters for the models which uses the full set of features for the manufacturing company dataset with three hierarchy levels. _Appl. Sci._**2020**, _10_, 1699 \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Values** \\ \hline utility score & all features from Table 3 \\ threshold & 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 \\ Jaccard value & 0.7, 0.8, 0.9, 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Hyperparameter search space for collective classification. \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}{*}{4 month} & \multirow{3}{*}{90\%} & \multicolumn{2}{c}{outdegree centrality} & 3 & 0.7 \\  & & eigenvector centrality & 3 & 0.7 \\  & & hubs centrality & 4 & 0.7 \\  & & clustering coefficient & 5 & 0.7 \\ \cline{1-1} \cline{2-5}  & \multirow{3}{*}{80\%} & hubs centrality & 4