# Complex Network Analytics for Structural-Functional Decoding of Neural Networks
## 2 Related Works
### Neural Network Structure Compression Pruning  
Neural network pruning improves computational efficiency by removing redundant parameters, and its core values are as follows: (1) reducing storage and computational resource consumption; (2) eliminating the noise interference of over-parameterised networks and improving the model generalisation ability; and (3) revealing the essence of the network expression and promoting the interpretability research [15]. According to the granularity of parameter removal, existing methods can be divided into two paradigms: structured pruning and unstructured pruning [16]. Unstructured pruning takes weights as the smallest unit of operation. Magnitude pruning, pioneered by [PERSON] et al. [5], achieves model scarification by removing low absolute weights, but its assumption of \"small weights = low importance\" may not work in complex networks. Subsequent research attempted to improve the weighting criterion, and [PERSON] et al. [6] proposed the lottery hypothesis theory and found that there are sparse sub-networks that can be trained independently, but sub-network identification relies on multiple parameter resets, which is computationally expensive. Structured pruning uses neurons/channels/layers as the unit of operation, but gradient amplitudes fail to distinguish channel synergy effects. [PERSON]'s team [17] developed a gradient-driven iterative pruning strategy that gradually eliminates minor units during training, but still fails to address the problem of topological integrity maintenance. He and [PERSON] [18] used node-degree centrality to guide channel pruning, and achieved a 12% higher performance than the traditional method on ResNet-50, in turn achieving 12% higher accuracy main tenance, initially verifying the effectiveness of graph theory-related methods. Overall, with the exponential growth of neural network model size, traditional layer-based analysis methods gradually expose the following limitations in practice: (1) invisible cross-layer interactions: it is difficult to track the nonlinear transformations of the features during cross-layer transfer [18]; (2) missing component correlation: ignoring the synergistic mechanism between different neurons [19]; (3) insufficient characterisation of dynamic properties: it is impossible to quantify the regularisation, normalisation etc. operations' dynamic effects [20]. Complex network theory provides new ideas to solve the above problems; therefore, this paper proposes a dual mapping mechanism of \"structure-function\", which achieves the following: First, it overcomes architectural fragmentation by providing cross-model compatibility for CNN, Dropout, and BatchNorm modules through standardised topological mapping rules. Second, it resolves the temporal mismatch in pruning decisions by incorporating dynamic topological evolution tracking. Third, it transcends statistical correlation by establishing causal structure-performance relationships through adaptive parameterisation techniques. Existing research works have three limitations: (1) fragmentation of heterogeneous network mapping methods and lack of a unified topological characterisation model; (2) pruning strategies are fragmented from structural analysis, failing to utilise dynamic topological evolution laws to guide compression; and (3) structure-performance correlation mechanisms are stuck at the level of statistical correlation and lack of a causal explanatory framework. In this paper, we establish a unified topological characterisation framework that bridges the fragmentation of heterogeneous network mapping methods.