# Complex Network Analytics for Structural-Functional Decoding of Neural Networks
## 4 Experiment and Result Analysis
### Experimental Analysis
#### 4.2.2 Node Analysis  
(1) Dynamic node ablation experiment  
In order to systematically investigate the structural plasticity of neural networks during training, this study adopts an adaptive time slicing strategy on SimpleCNN, DropoutCNN, and BatchNormCNN models trained on the CIFAR-10 dataset. The method successfully transforms gradient spatial features into time-dimensional representations by correlating the loss landscape geometric features with the temporal sampling density--automatically triggering high-frequency sampling when the loss curvature exceeds the threshold \(\kappa=0.15\). This adaptive sampling strategy achieves Pareto optimality between storage efficiency and topological integrity, overcoming the defects of fixed-interval sampling that oversamples the plateau period or misses key bifurcation points. The network structure is pruned based on the accuracy-efficiency trade-off results, and then transformed into a neural network for comparative experiments to analyse the impact of the network structure on the correct rate. (2) Accuracy-efficiency trade-off analysis  
In the training phase, different models are dynamically selected to preserve the dynamic evolution characteristics of all nodes according to the loss surface, as shown in Figure 2.  
Figures show that the training progression charts collectively reveal distinct architectural behaviours through their loss and accuracy trajectories. The preservation points of the three types of network models placement strategy consistently demonstrates three crucial functions across all models: first, it captures architecture-specific inflection points like SimpleCNN's performance ceiling; second, it documents regularisation-induced instability events in Dropout's training; third, it maintains fidelity in recording BatchNorm's optimised convergence. Particularly noteworthy is how checkpoint markers align with Dropout's accuracy oscillations and SimpleCNN's transition to diminishing returns, while in BatchNorm they simply confirm steady progress--proving that the sampling interval adapts to each model's unique topological rhythms. This comprehensive visualisation approach successfully balances monitoring density with analytical precision, preserving each architecture's signature learning behaviour while enabling retrospective diagnosis of topological reorganisation events through strategic snapshot placement. Meanwhile, the comparative analysis of the intensity values of individual nodes under different stages is shown in Figure 3. At the same time, the network strength values are ranked, from which the activation threshold \(\theta\) is determined as the discriminative criterion for pruning. Figure 3: The strength values of nodes in different networks at different stages: (**a**) SimpleCNN model; (**b**) Dropout model; (**c**) BatchNorm model. Figure 2: The training results of 3 different network models. (**a**) SimpleCNN model; (**b**) Dropout model; (**c**) BatchNorm model. Most of BatchNormCNN is located in the shallow convolutional channel, while DropoutCNN is concentrated in the fully connected layer, and SimpleCNN shows uniform distribution characteristics across layers, which is highly consistent with the path of action of the regularisation mechanism of each model. Node strength distributions across three neural network architectures systematically reveal their topological evolution characteristics and implications for pruning strategies. SimpleCNN exhibits a stable bimodal structure (peaks at 0.05/0.25), with low-strength nodes (<0.1) constituting >55% of the network, indicating its static topological nature. An activation threshold 0.05, determined from the strength-sorted curve, enables significant parameter compression, validating the redundancy of low-strength nodes in rigid architectures. The Dropout model demonstrates dynamic three-phase evolution: oscillation, reorganisation, and stable phase, during which high-strength nodes progressively migrate to fully connected layers.