# XGBoost-Enhanced Graph Neural Networks: A New Architecture for Heterogeneous Tabular Data
## 4 Experiment
### Training Time  
Previous experiments demonstrated that XGNN performs very well on a variety of datasets, so it was important to demonstrate whether XGBoost models increase training time. With early stopping and learning rate adjustment, we measured clock time to train each model until convergence. Table 5 gives the training times for all models. The experimental results show that XGNN runs faster than GNN in most cases. This demonstrates that the XGNN model, when combined with the XGBoost model, does not require extra training time and is actually more efficient than the GNN. For example, for the County dataset, XGNN is more than nine times faster than GNN. The main reason for this is that XGBoost and GNN significantly improve parallel performance by utilizing the GPU's parallel computing power to handle the segmentation process of multiple data samples at the same time. Moreover, the GPU histogram parameter algorithm using XGBoost can store the data in the graphics memory, which is much faster than storing the data in the main memory.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **House** & **County** & **VK** & **Avazu** & **Wiki** \\ \hline CatBoost & 0.63 \(\pm\) 0.01 & 1.39 \(\pm\) 0.07 & 7.16 \(\pm\) 0.20 & 0.1172 \(\pm\) 0.02 & 46,359 \(\pm\) 4508 \\ LightGBM & 0.63 \(\pm\) 0.01 & 1.4 \(\pm\) 0.07 & 7.16 \(\pm\) 0.20 & 0.1171 \(\pm\) 0.02 & 49,915 \(\pm\) 3643 \\ GCN & 0.63 \(\pm\) 0.01 & 1.48 \(\pm\) 0.08 & 7.25 \(\pm\) 0.19 & 0.1141 \(\pm\) 0.02 & 44,936 \(\pm\) 4083 \\ GAT & 0.54 \(\pm\) 0.01 & 1.45 \(\pm\) 0.06 & 7.22 \(\pm\) 0.19 & 0.1134 \(\pm\) 0.01 & 45,916 \(\pm\) 4527 \\ APPNP & 0.69 \(\pm\) 0.01 & 1.5 \(\pm\) 0.11 & 13.23 \(\pm\) 0.12 & 0.1127 \(\pm\) 0.01 & 53,426 \(\pm\) 4159 \\ AGNN & 0.59 \(\pm\) 0.01 & 1.45 \(\pm\) 0.08 & 7.26 \(\pm\) 0.20 & 0.1134 \(\pm\) 0.02 & 45,982 \(\pm\) 3058 \\ FCNN & 0.68 \(\pm\) 0.01 & 1.48 \(\pm\) 0.07 & 7.29 \(\pm\) 0.21 & 0.118 \(\pm\) 0.02 & 51,662 \(\pm\) 2983 \\ F-GNN & 0.53 \(\pm\) 0.01 & 1.39 \(\pm\) 0.06 & 7.22 \(\pm\) 0.20 & 0.1114 \(\pm\) 0.02 & 48,491 \(\pm\) 7889 \\ BestovNN & 0.467 \(\pm\) 0.01 & 1.145 \(\pm\) 0.08 & 6.918 \(\pm\) 0.22 & 0.105 \(\pm\) 0.01 & - \\ RES-GNN & 0.51 \(\pm\) 0.01 & 1.33 \(\pm\) 0.08 & 7.07 \(\pm\) 0.20 & 0.1095 \(\pm\) 0.01 & 46,747 \(\pm\) 4639 \\ XGNN & 0.45 \(\pm\) 0.01 & 1.09 \(\pm\) 0.08 & 6.847 \(\pm\) 0.20 & 0.097 \(\pm\) 0.01 & 49,222 \(\pm\) 3743 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy of node classification for different datasets.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **House** & **County** & **VK** & **Avazu** & **Wiki** \\ \hline CatBoost & 0.63 \(\pm\) 0.01 & 1.39 \(\pm\) 0.07 & 7.16 \(\pm\) 0.20 & 0.1172 \(\pm\) 0.02 & 46,359 \(\pm\) 4508 \\ LightGBM & 0.63 \(\pm\) 0.01 & 1.4 \(\pm\) 0.07 & 7.16 \(\pm\) 0.20 & 0.1171 \(\pm\) 0.02 & 49,915 \(\pm\) 3643 \\ GCN & 0.63 \(\pm\) 0.01 & 1.48 \(\pm\) 0.08 & 7.25 \(\pm\) 0.19 & 0.1141 \(\pm\) 0.02 & 44,936 \(\pm\) 4083 \\ GAT & 0.54 \(\pm\) 0.01 & 1.45 \(\pm\) 0.06 & 7.22 \(\pm\) 0.19 & 0.1134 \(\pm\) 0.01 & 45,916 \(\pm\) 4527 \\ APPNP & 0.69 \(\pm\) 0.01 & 1.5 \(\pm\) 0.11 & 13.23 \(\pm\) 0.12 & 0.1127 \(\pm\) 0.01 & 53,426 \(\pm\) 4159 \\ AGNN & 0.59 \(\pm\) 0.01 & 1.45 \(\pm\) 0.08 & 7.26 \(\pm\) 0.20 & 0.1134 \(\pm\) 0.02 & 45,982 \(\pm\) 3058 \\ FCNN & 0.68 \(\pm\) 0.01 & 1.48 \(\pm\) 0.07 & 7.29 \(\pm\) 0.21 & 0.118 \(\pm\) 0.02 & 51,662 \(\pm\) 2983 \\ F-GNN & 0.53 \(\pm\) 0.01 & 1.39 \(\pm\) 0.06 & 7.22 \(\pm\) 0.20 & 0.1114 \(\pm\) 0.02 & 48,491 \(\pm\) 7889 \\ BestovGNN & 0.467 \(\pm\) 0.01 & 1.145 \(\pm\) 0.08 & 6.918 \(\pm\) 0.22 & 0.105 \(\pm\) 0.01 & - \\ + C\&S & & & & \\ RES-GNN & 0.51 \(\pm\) 0.01 & 1.33 \(\pm\) 0.08 & 7.07 \(\pm\) 0.20 & 0.1095 \(\pm\) 0.01 & 46,747 \(\pm\) 4639 \\ XGNN & 0.45 \(\pm\) 0.01 & 1.09 \(\pm\) 0.08 & 6.847 \(\pm\) 0.20 & 0.097 \(\pm\) 0.01 & 49,222 \(\pm\) 3743 \\ \hline \hline \end{tabular}
\end{table}
Table 3: RMSE of node prediction for different datasets.