# Complex Network Analytics for Structural-Functional Decoding of Neural Networks
## 4 Experiment and Result Analysis
### Experimental Analysis
#### 4.2.2 Node Analysis
The pruning strategy adopts an iterative amplitude pruning method and the structured pruning is performed every 2 epochs until 80% sparsity is reached; the experimental results are shown in Figure 5.  
Through the experiments, it was found that when the combination of progressive pruning and dynamic retraining is used, the performance of the pruned model shows significant advantages: the final accuracy of the model after pruning of the SimpleCNN network reaches 82.80%, which is 2.63% higher than that of the original model, confirming that the pruning can assist the model in jumping out of the local optimal solution of the  
Figure 5: The comparison results of retraining before and after network pruning. original optimisation trajectory; the model after pruning of the DropoutCNN network model reaches 81.08% accuracy after pruning, improving 1.88% compared to the original model, but there is a phased accuracy fluctuation in the training process, revealing that the interaction sensitivity between the retained parameters and the Dropout mask is enhanced after pruning; BatchNormCNN shows adaptive improvement and the accuracy of both the original model and the pruned model decreases after retraining, but the accuracy after pruning is 78.84%, 2.52% higher to than that of the original model. Its batch normalisation layer effectively enhances the information transmission efficiency of the key channels by dynamically adjusting the standard deviation of the feature distribution. Figure 5 systematically demonstrates the performance-enhancing effect of post-pruning retraining through three comparative line graphs. For SimpleCNN, the post-pruning training accuracy surpasses both original training and the baseline model, achieving peak performance within just 7 training epochs--confirming the efficient recovery characteristics inherent to linear topological architectures. DropoutCNN exhibits phase-specific oscillations during initial retraining, reflecting temporal costs associated with stochastic path reconstruction. Most significantly, BatchNormCNN shows remarkable performance amplification, ascending continuously from its initial post-pruning accuracy, demonstrating that pruning-induced removal of distributional interference sources accelerates \(\gamma/\beta\) parameter optimisation in BatchNorm layers. Collectively, these models establish post-pruning retraining as a performance amplification engine whose efficacy scales proportionally with architectural complexity. This phenomenon may stem from two mechanisms: firstly, the pruning process forces the network to focus feature representations on key connections, which effectively reduces the risk of overfitting caused by redundant parameters; secondly, the channels retained after structured pruning have stronger semantic interpretability, and the strength of activation response of the pruning model in the key feature regions is dramatically improved. In addition, to illustrate the advantages of the method proposed in this paper, we conducted an empirical comparison of dynamic topological pruning on three types of network models using the classical method, L1 pruning, and the lottery assumption. The results are shown in Table 3.  
At a pruning rate of 55.5% (compared to 50% for L1/LTH), the proposed method demonstrates comprehensive advantages: it achieves a significant accuracy improvement in SimpleCNN (82.80%), synergistic robustness with regularisation in DropoutCNN (81.08%), and effective mitigation of distribution shift in BatchNormCNN (1.72% decline). Across all three models, the method delivers positive gains or minimised losses, with a significantly narrower performance fluctuation range than L1 and LTH, confirming its superior generalisation capability. This analysis shows that the combination of structured pruning and targeted retraining can effectively improve the model performance, with the batch normalised architecture showing the strongest adaptability.