## 2 Materials and Methods
### Interpretability and Feature Importance
#### 2.7.1 Model Interpretability

Table 6: Attributes used to analyze the projects in the global consulting company and international manufacturing company. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Distribution of Staff in Two Levels**} \\ \hline
**Projects** & **Leadership Level** & **Working Level** \\ \hline C & 21 & 25 \\ D & 21 & 25 \\ E & 25 & 52 \\ F & 25 & 52 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The number of staff in leadership level and working level in the global consulting company and international manufacturing company. ## 4 Experiments and Results

### Prediction Results

We mainly worked on a real-world project and two projects at an international manufacturing company. After we extracted the required features, as mentioned in Section 2, we employed XAI to identify the most critical features for the subsequent training process. We used statistical machine learning methods, graph neural networks, and AutoML models to train on these eight projects for comparison. Specifically, the three statistical machine learning algorithms are support vector machines (SVMs) with three different kernels, k-nearest neighbors (k-NN), and multilayer perceptron (MLP), as we discussed before. The graph convolutional network (GCN) is the graph neural network we utilized, and we set the GCN with the size of hidden neurons to two configurations: GCN model with the number of 256 and 1024 neurons, respectively. We also compared the outcomes of all employed techniques. To measure the efficiency improvement with features by adding PageRank and word embeddings, our statistical machine learning methods were fit with various combinations of features. Table 8 presents a comparative analysis for training with various feature combinations on different machine learning methods. Most statistical machine learning models have shown significant improvements in accuracy. In project A, as seen in Table 8, the addition of PageRank values results in accuracy improvements of up to 1 percent for all models. However, the inclusion of word embedding values can yield improvements of up to 8 percent. When both PageRank and word embedding values are added together, all models achieve their highest accuracy. Notably, SVM-G exhibits the most substantial improvement, increasing from 71.52% to 82.31%. Project B also achieves the highest accuracy with Pagerank and word embedding values together. The experiment demonstrates that the accuracy of different models can be optimized by integrating original features with PageRank and word embeddings. Figure 6: Interaction graphs based on the six types of inter-colleague relationships in project E. Graphs (**a–f**) show the interaction graphs of relationships (1) to (6), respectively. The results obtained from the GCN experiments are presented in Table 9. In project A, the changing size of hidden neurons between 256 and 1024 has a minimal impact on performance. The lowest accuracy of 82.09% is achieved using features with 256 neurons. The highest accuracy of 84.06% is achieved when using PageRank values with 1024 neurons. This accuracy is even higher than training with features, PageRank, and word2 vec together. Project B achieves higher accuracy with 1024 neurons than 256 neurons as well. The graph convolutional network excels in accuracy by extracting adequate information from the edge intensities between nodes. Generally, the accuracy achieved by GCN is relatively higher than that achieved by statistical machine learning methods. 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.