# Dynamic Graph Attention Network for Skeleton-Based Action Recognition
## 4 Experiments
### Ablation Studies
#### 4.4.1 Effectiveness of Each Component  
Firstly, we evaluate the performance of the model under various combinations of the two main components: spatial and temporal modules. As shown in Table 3, we compare combinations based on attention and temporal convolution (TCN), Graph Attention Network with TCN, DGAN + TCN, and their corresponding variants using MSTC. The results indicate that employing MSTC significantly enhances performance compared to conventional single-scale temporal convolution. Regardless of the spatial attention mechanism used, the accuracy improves by approximately 1.7-1.8% after integrating MSTC, highlighting MSTC's effectiveness in capturing both short-term and long-term dependencies within action sequences. On the other hand, given the same temporal module, different spatial modeling strategies also exhibit performance variations. The proposed DGAN achieves the best performance in spatial relationship modeling by employing a dynamically learned adjacency matrix. When combined with the MSTC module, it reaches an accuracy of 90.90%. In comparison, the GAT+MSTC model, which relies on a static baseline adjacency matrix, achieves 90.46%, while the Attention+MSTC model, based on global self-attention, achieves 90.62%. Similarly, when using standard TCN alone, DGAN+TCN outperforms GAT+TCN and attention+TCN by approximately 0.5% and 0.3%, respectively. These findings suggest that DGAN can more effectively exploit spatial dependencies inherent in skeleton data compared to conventional graph convolution or pure self-attention mechanisms, thereby enhancing discriminative feature representation.  
To determine the optimal model combination, we conducted additional experiments based on the DGAN+MSTC combination, recording the performance variations as presented in Table 4. First, introducing an additional MLP module DGAN+MSTC+MLP reduced the accuracy from 90.9% to 89.27%, indicating that simply stacking fully connected layers does not yield additional benefits and instead introduces unnecessary parameters and potential overfitting risks. Secondly, we tested a temporal modeling strategy using a DGAN-like mechanism instead of MSTC, resulting in a significant accuracy decrease to 88.26%, underscoring that multi-scale convolution in the temporal dimension is essential for effectively capturing action sequences; relying solely on graph attention is insufficient for temporal feature extraction. Therefore, DGAN and MSTC complement each other, synergistically enhancing overall performance. Inspired by the Timesformer model [3], we also experimented with reversing the order of temporal and spatial modeling--performing temporal modeling before spatial attention--but observed a notable accuracy decline. Consequently, this study adopts the spatial-before-temporal modeling strategy, which empirically demonstrates superior effectiveness.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Model** & **Accuracy (\%)** \\ \hline Attention+TCN & 88.93 \\ GAT+TCN & 88.64 \\ DGAN+TCN & 89.21 \\ Attention+MSTC & 90.62 \\ GAT+MSTC & 90.46 \\ DGAN+MSTC & 90.90 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effectiveness of each component of DGAN on NTU RGB+D bone modality X-Sub.