## 3 Results
### Ablation Experiment  
We conducted two ablation experiments to assess how node features and the attention mechanism contribute to the performance of the GBNSS model. Specifically, we evaluated the effect of topological features and biochemical features on two similarity search tasks: topological similarity and biochemical similarity. These experiments were designed to determine how each component influences the model's predictions, with the Wilcoxon signed-rank test used to assess the statistical significance of the results.  
To ensure a focused analysis, all experiments were conducted on the Homo Sapiens dataset, a widely used and comprehensive biological network. This choice minimizes the influence of dataset characteristics, allowing us to isolate the effects of the model components.  
The first experiment analyzed the impact of topological node features and the attention mechanism on the model's ability to predict topological similarity. The four model configurations and their results are summarized in Table 4.  
The full model GBNSS achieves the highest topological similarity score of 0.304, demonstrating that the combined use of topological features and an attention mechanism enhances performance. In contrast, \(GBNSS_{\ell}\)performs the worst, with a score of 0.191, indicating that the absence of both components negatively impacts the model's predictions.  
The intermediate scores for \(GBNSS_{\ell}\)2 (0.239) and \(GBNSS_{\ell}\)3 (0.220) suggest that topological features and attention mechanisms each independently contribute to performance improvements, but the greatest accuracy is achieved when both are used together.  
We used the Wilcoxon signed-rank test to compare the full model with each ablated version. The results show that both topological features and the attention mechanism enhance performance, with the attention mechanism providing a statistically significant improvement (\(p<0.05\)).  
The second experiment examined how biochemical node features and the attention mechanism affect the model's ability to predict biochemical similarity. The four model configurations and their results are presented in Table 5.  
The full model GBNSS achieves the highest biochemical similarity score of 0.75, indicating that both biochemical features and attention mechanisms are essential for optimal performance. The \(GBNSS_{\ell}\)1 configuration produces the lowest score (0.43), showing that the absence of both components significantly reduces prediction accuracy.  
The intermediate scores of \(GBNSS_{\ell}\)2 (0.56) and \(GBNSS_{\ell}\)3 (0.51) suggest that both biochemical features and attention mechanisms independently enhance performance. However, the combination of both components yields the best results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Features** & **Attention Mechanism** & **Result** \\ \hline \(GBNSS_{\ell}\)1 & None & NO & 0.191 \\ \(GBNSS_{\ell}\)2 & None & Yes & 0.239 \\ \(GBNSS_{\ell}\)3 & TOF & NO & 0.22 \\ \(GBNSS\) & TOF & YES & 0.304 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Attention mechanism and features used in different GBNSS models about topological similarity searches in Bos Taurus species.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **PathEmb** & **NSSRF2** & **NSSRF3** & **NSSRF4** & **GBNSS** \\ \hline Bos Taurus & 2.4 & 2.1 & 2.5 & 2.3 & 0.14 \\ Homo Sapiens & 39.6 & 3.4 & 10 & 9.2 & 0.2 \\ Mus Musculus & 36.5 & 2 & 5.5 & 6.1 & 0.2 \\ Saccharomyces c. & 11.3 & 2.2 & 3 & 2.9 & 0.16 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The similarity search querying time.  
We used the Wilcoxon signed-rank test to compare the full model with each ablated version. The results confirm that both biological features and the attention mechanism contribute meaningfully to performance improvements, with each showing a statistically significant effect (\(p<0.05\)).