# Complex Network Analytics for Structural-Functional Decoding of Neural Networks
## 4 Experiment and Result Analysis
### Experimental Analysis
#### 4.2.2 Node Analysis
Phase-adaptive thresholds 0.04 achieve a high pruning ratio, confirming that stochastic reconstruction enhances topological homogeneity. For the BatchNorm model, Gaussian evolution emerges: high-strength nodes form spatial functional clusters in shallow convolutional layers. A constant threshold 0.07 is optimal due to the stable proportion of the low-strength domain in Gaussian distributions and the necessity to preserve functional clusters intact. In this study, we propose a dynamic strength threshold pruning method to analyse the strength spectrum of the whole network nodes. A total of 343 low-activation nodes are identified by SimpleCNN network with activation threshold \(\theta=0.05\), 318 low-activation nodes are identified by DropoutCNN network with activation threshold \(\theta=0.04\), and 315 low-activation nodes are identified by DropoutCNN network with activation threshold \(\theta=0.07\). Based on this, the networks were pruned, retaining the connection structure but setting the weights to zero. The strength values of these nodes were consistently below the threshold throughout the training cycle, and the variance of strength fluctuations was only 17.3% of that of the highly activated nodes, indicating a lack of stability in their information transfer function. After pruning the three networks, we compared their accuracy rates, and the results are shown in the Figure 4.  
Figure 4: The test accuracy rates after pruning of different networks. Figure 4 clearly demonstrate that pruning operations impose significant performance degradation across all three network architectures, with an average accuracy reduction of 6.8%. SimpleCNN exhibits superior pruning resilience due to its robust feature backbone integrity, where pruning primarily removes localised redundant pathways. Conversely, regularised architectures (DropoutCNN and BatchNormCNN) amplify information loss post-pruning because of their dependencies on statistical parameters and stochastic path disruption mechanisms. An analysis of the pruning patterns of the three network architectures reveals that the pruned nodes are not uniformly distributed and the pruning of BatchNormCNN is mainly concentrated in the shallow convolutional channel, with the second convolutional layer pruned by 98.4%, which is significantly higher than that of the DropoutCNN and SimpleCNN networks. This suggests that the batch normalisation operation enhances parameter redundancy in the convolutional layer, and, at the same time, the BatchNormCNN presents a spatially continuous distribution of key nodes retained in the first convolutional layer, suggesting that batch normalisation may guide the network to form a structured feature representation pattern. In terms of the fully connected layer, DropoutCNN relatively retains more key nodes, confirming the protective effect of the dropout mechanism on important nodes, while SimpleCNN presents a high percentage of pruning in both the convolutional and fully connected layers due to the lack of regularisation mechanism. The nodes in the fc2_out layer of all three network models are completely clipped, revealing the presence of severe over-parameterisation in the classification layer. These differences suggest that the regularisation mechanism deeply affects the importance distribution of network parameters, batch normalisation tends to produce highly substitutable convolutional features, and dropout is more effective in maintaining the node diversity of the fully connected layer. These three typical architectures are selected, the pruned network is compared with the original network, and 10 epochs of training are carried out at the same time to analyse the results of the model test accuracy after the completion of the training.