## 5 Experimental Results and Analysis
### Result Evaluation  
Figure 6 demonstrates the experimental results of VNR acceptance ratio under four algorithms. The acceptance ratio of the four algorithms all fluctuate greatly during the initial mapping stage and gradually decrease over time. In the initial stage of mapping, the newly arrived VNR has sufficient available satellite network resources, so the embedding success ratio is high. Over time, the limited resources of the physical network are occupied by successfully embedded VNRs, and the embedding success ratio of newly arrived VNRs becomes low because there are not enough resources to carry them. Finally, as the system gradually reaches an equilibrium state of resource utilization to adapt to long-term resource constraints, the embedding success ratio levels off. However, our algorithm has a better VNR acceptance ratio than the other three algorithms from the beginning of mapping to any time period thereafter, which is 3.81%, 7.76%, and 12.13% higher than DDRL-VNE, CDRL, and DSCD-VNE, respectively. The proposed SN-VNE uses graph neural network to extract node link features, optimizes the mapping process, and improves the VNR acceptance ratio. The highly dynamic network cannot be satisfied during link mapping for DSCD-VNE algorithm by shortest path, so the VNR acceptance ratio is low. DDRL-VNE trains the agent to obtain the node mapping method, while getting the link embedding  
Figure 5: The long-term average revenue/cost of policy network training process.  
method by the shortest path; it cannot adapt to highly dynamic networks. CDRL designs a mapping scheme through the classic seq2 seq model based on reinforcement learning, but the algorithm lacks flexibility, so the VNR acceptance ratio is low.  
Figure 7 shows the four algorithms experimental results of the long-term average revenue. At the beginning, all the curves are high. It is worth noting that the abundant physical network resources in the early stage of testing can successfully map VNRs. However, as the mapped VNR takes up network resources, the success ratio of VNR mapping begins to decline, and the long-term average revenue of the four algorithms declines. Finally, the system gradually reaches a balanced state of resource utilization to adapt to the situation of limited long-term resources, the curve tends to be stable. The revenue of the proposed algorithm is 4.72%, 11.63%, and 31.25% higher than that of DDRL-VNE, CDRL, and DSCD-VNE, respectively. Compared with DDRL-VNE, CDRL, and DSCD-VNE, the proposed algorithm accurately perceives the physical network through graph convolution, improves resource utilization efficiency under limited network resources, and obtains higher revenue.  
Figure 8 depicts the latency performance of the four algorithms at different points in time. Conclusions can be drawn from the figure. The latency performance of SN-VNE is superior to the other three methods. The SN-VNE algorithm takes latency as a part of the reward function and is directly related to model performance, while other algorithms are not. Through experiments, the average delay of SN-VNE has achieved good optimization effect, which is 52.73%, 67.36%, and 71.52% lower than that of DDRL-VNE, DSCD-VNE, and CDRL, respectively.  
Figure 6: The acceptance ratio of the testing process.  
Figure 8: The average delay of testing process.  
Figure 7: The long-term average revenue of the testing process.