## 4 Experiment
### Parameter Analysis Experiment  
In this section, we analyze the impact of different parameter variables on the performance of the SSP-AA model through changes in the high-order neighbor parameters and model depth. The experimental results show the average AUC and relative error of five model runs. (1) For link prediction tasks, the performance of the SSP-AA model is influenced by the selection of high-order neighbors. Figure 8 shows the impact of high-order neighbor parameters within the range of [1; 5] on model performance in four datasets. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Modules** & **Index** & **Celegans** & **NS** \\ \hline SSP-AA & AUC & 88.52 \(\pm\) 0.49 & 99.48 \(\pm\) 0.09 \\ No Jumping Knowledge & AUC & 88.33 \(\pm\) 0.45 & 99.18 \(\pm\) 0.09 \\ No Adaptive Attention & AUC & 87.63 \(\pm\) 0.67 & 98.31 \(\pm\) 0.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Result of the module ablation study. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Modules** & **Index** & **Yeast** & **PB** \\ \hline SSP-AA & AUC & 97.89 \(\pm\) 0.10 & 94.80 \(\pm\) 0.12 \\ Not based on subgraphs & AUC & 93.39 \(\pm\) 1.28 & 91.26 \(\pm\) 0.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Result of the subgraph ablation study. The results show that when the high-order neighbor parameter is set to 1, the number of nodes sampled by the model is relatively small. Some important node information is lost, and the hidden information of high-order neighbors is not utilized, leading to generally lower model performance. However, as the parameter increases, the performance of the model also improves accordingly. It is worth noting that although more neighboring nodes are selected, the performance of the model does not improve in most datasets. Instead, when the high-order neighbor exceeds 2, the performance of the model decreases. The reason for this trend is that these datasets are sparse, and increasing the range of high-order neighbors does not improve model performance. Additionally, sampling more distant neighbor nodes will introduce noise, which in some datasets will lead to a decline in the final model performance when sampling higher-order neighbor nodes. Therefore, considering the performance of the model on various datasets, it is not advisable for the selection of high-order neighbors in the proposed SSP-AA model to exceed 2. (2) The SSP-AA model is influenced by changes in model depth. Table 6 displays the impact of different depths of the jumping knowledge module in the model on the performance across two different datasets. Table 6 shows that in three different datasets, the performance is lower when the model depth is in the range of [1, 2]. The model performance improves the most when the model depth is 3. When the model depth is in the range of [4, 8], the change in the model's performance across three different datasets is minimal and tends toward stability. It is due to the fact that the SSP-AA model lacks sufficient depth to extract complex structural features and high-order neighbor information from the graph, resulting in lower model performance when the model depth is shallow. As the depth further increases, the model's performance gradually improves. This is because as the model depth increases, the jumping knowledge module enables the information from deep nodes to be directly combined with the information from shallow nodes. Even if the network depth becomes very deep, the