## 2 Materials and Methods
### Interpretability and Feature Importance
#### 2.7.1 Model Interpretability
All experiments were repeated 10 times with randomized training and test sets, and we calculated the average accuracy for a robust estimation of overall performance. ### Leader-Follower Dynamics

Our work incorporated leader-follower dynamics, as can be seen from Figures 5 and 6, and Tables 5 and 7. It describes how individuals within a network relate to each other, with certain members as leaders while others follow their guidance ([PERSON] et al. [74]). Understanding this dynamic is important for grasping influence, power structures, and decision-making processes within social systems. Its key components include role identification, network structure, and dynamics of influence. Leader-follower dynamics offer key insights into influence within social networks. It also helps researchers better comprehend decision-making processes among different social contexts ([PERSON] et al. [75]). Our GCN model uses the library GraphConv. GraphConv is a library designed to implement graph convolutional networks as well as other graph-based deep learning models. It updates the rep \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Projects** & **Features** & **SVM-G** & **SVM-L** & **SVM-S** & **K-NN** & **MLP** \\ \hline \multirow{4}{*}{Project A} & F & 0.7152 & 0.7089 & 0.7042 & 0.7196 & 0.7106 \\  & F+P & 0.7171 & 0.7098 & 0.7093 & 0.7225 & 0.7129 \\  & F+W & 0.8002 & 0.7819 & 0.7143 & 0.7923 & 0.7349 \\  & F+P+W & 0.8231 & 0.8053 & 0.7685 & 0.8029 & 0.7371 \\ \hline \multirow{4}{*}{Project B} & F & 0.7761 & 0.7758 & 0.7869 & 0.7851 & 0.7769 \\  & F+P & 0.7810 & 0.7764 & 0.7866 & 0.7819 & 0.7774 \\ \cline{1-1}  & F+W & 0.8941 & 0.8884 & 0.8983 & 0.8899 & 0.8567 \\ \cline{1-1}  & F+P+W & 0.8891 & 0.9047 & 0.8878 & 0.8896 & 0.8581 \\ \hline \hline \end{tabular}

* The statistical machine learning methods are SVM with Gaussian (SVM-G), linear (SVM-L), sigmoid kernel (SVM-S), K-nearest neighbors (K-NN), and multilayer perceptron (MLP).

\end{table} 
Table 8: Model accuracy by statistical machine learning approach with 4 groups of features: features (F), features + PageRank (F+P), features + word2 vec (F+W), and features + PageRank + word2 vec (F+P+W) in projects A and B from the IT company. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Projects** & **Size of Neurons** & **F** & **F+P** & **F+W** & **F+P+W** \\ \hline \multirow{2}{*}{Project A} & 256 & 0.8209 & 0.8225 & 0.8308 & 0.8262 \\  & 1024 & 0.8326 & 0.8406 & 0.8225 & 0.8210 \\ \hline \multirow{2}{*}{Project B} & 256 & 0.8818 & 0.8853 & 0.8876 & 0.8982 \\  & 1024 & 0.8823 & 0.8861 & 0.8921 & 0.9064 \\ \hline \hline \end{tabular}

* In our experiments, we demonstrated that incorporating features derived from PageRank and word2 vec values enhances classification accuracy, benefiting both statistical machine learning techniques and GCN. Among all the methods employed, GCN outperforms the best, with an accuracy rate of 84.06% in project A. On the other hand, statistical machine learning methods achieve the highest accuracy of 82.31%, which is lower than that of GCN. All experiments were repeated 10 times with randomized training and test sets, and we calculated the average accuracy for a robust estimation of overall performance.

\end{table} 
Table 9: Model accuracy by graph convolutional network with 4 groups of features: features (F), features + PageRank (F+P), features + word2 vec (F+W), and features + PageRank + word2 vec (F+P+W) in projects A and B from the IT company. resentation of each node by aggregating the features of the node with those of its neighboring nodes. Regarding directed graph models, the graphs we employ in six projects are all directed graphs. The application of directed graphs is common in graph convolutional networks. By using directed edges, information flow within the graph structure can be captured more precisely when the relationships between nodes are directional ([PERSON] et al. [76]).