## 4 Experiments
### Experimental Design
#### 4.2.5 Model Comparison Experiments  
In this section, we compare various graph embedding techniques and fusion mechanisms to evaluate their performance on scene graph representation and urban economic activity prediction. The results are systematically analyzed using statistical metrics such as accuracy, F1 score, and cross-entropy loss. Graph Embedding ComparisonTo assess the effectiveness of different graph embedding methods in representing scene graphs, we evaluated the performance of GraphSAGE and Node2 Vec [17]. The comparison was based on several metrics, including node count, graph density, accuracy, and graph distance. As shown in Table 3, GraphSAGE outperforms Node2 Vec, achieving a higher accuracy (85.2%) and a lower graph distance (0.15), indicating better retention of spatial relationships and structural integrity within the scene graphs. GraphSAGE operates in a message-passing paradigm, preserving spatial and topological properties of the graph. This contrasts with Node2 Vec, which relies on random walks to capture node proximity, often losing fine-grained structural details. GraphSAGE can \begin{table}
\begin{tabular}{c c c c c} \hline
**Optimizer** & **Learning Rate** & **Weight Decay** & **Batch Size** & **Hardware** \\ \hline SGD & 0.0001 & 0.0005 & 12 & RTX 4090 \\ Adam & 0.006 & 0.0001 & 12 & RTX 4090 \\ RMSprop & 0.005 & 0 & 12 & RTX 4090 \\ \hline \end{tabular}
\end{table} 
Table 2: Segmentation model training settings. generalize to unseen nodes, as it does not rely on precomputed embeddings but instead computes them dynamically using the graph structure. This inductive property is particularly useful in dynamic or incomplete scene graphs, where new nodes or edges may be introduced during inference. Node2 Vec generates embeddings based on node proximity using biased random walks. While this captures high-level graph topology, it struggles to encode the detailed relationships and interactions that are essential for scene graph tasks. The results demonstrate that while both methods are comparable in terms of node count and graph density, GraphSAGE significantly excels in accuracy and preserving graph structure. This suggests that GraphSAGE is better suited for tasks requiring high spatial resolution in graph-based representations. The comparison of experimental results between five models, including SwinV2-B, SwinUnet, DS-TransUNet-B, CSwin-B, and our proposed method, demonstrates the effectiveness of our approach across various evaluation metrics, namely accuracy, recall, F1 score, throughput, FLOPS, and the number of parameters. According to the analysis of the results in Table 4, our method outperforms the baseline model in both accuracy and F1 score. Specifically, our method achieves 91.5% accuracy, which is a 2.2% improvement over CSwin-B with the second highest accuracy and a 2.3% and 3% improvement over SwinUnet and SwinV2-B, respectively. Furthermore, our method achieves an F1 score of 88.9%, compared with CSwin-B's F1 score of 88.8%. However, the F1 scores of SwinUnet and SwinV2-B are 87.2% and 86.7%, respectively. Compared with DS-TransUNet-B, our method improves the accuracy by 4.2% and improves the F1 score by 2.1%. Our model also exhibits a notable advantage in throughput, achieving 74/s, significantly outperforming the competing models. CSwin-B and DS-TransUNet-B reach throughput levels of 60/s and 61/s, respectively, while SwinUnet and SwinV2-B record 56/s and 67/s. The increased throughput demonstrates the efficiency of our method in processing a large number of samples in a short amount of time, making it suitable for practical deployment scenarios. Although our model's FLOPs reach 52.3 G, slightly higher than CSwin-B (46.7 G) and SwinUnet (48.5 G), the significant performance improvements justify this computational cost. Furthermore, compared with models with lower FLOPS, such as DS-TransUNet-B and SwinV2-B, our method achieves higher efficiency and predictive performance, demonstrating a well-optimized balance between computational complexity and effectiveness.