## 4 Discussion  
This study explores the application of various complex network techniques for non-invasive Hb detection. The findings indicate that utilizing the clustering results of multi-sample weighted undirected graphs as features for Hb interval classification significantly enhances performance, demonstrating the effectiveness of these clustering outcomes in facilitating Hb interval categorization. Moreover, in a multi-sample weighted undirected graph composed of morphological features of multiple PPG signals, four clusters can be obtained by using [PERSON]'s algorithm to cluster the network. Comparison of the clustering results with the Hb index shows that the clusters are highly correlated with the physiological index of Hb, which indicates that the morphology of PPG signals is highly correlated with the Hb index, and the clustering results based on the morphology of PPG signals can also reflect the interval distribution of Hb. Furthermore, the comparison experiments reveal that while visibility graph methods exhibit some beneficial effects in the field of Hb detection, their contribution to improving the accuracy of Hb concentration measurement is limited. Among the visibility graph transformation methods, the Natural Visibility Graph (NVG) outperforms the Horizontal Visibility Graph (HVG) in Hb detection. The inferior performance of HVG compared to NVG can be attributed to HVG's tendency to discard edge connections during the transformation process to expedite the generation of complex networks [36]. While this characteristic reduces transformation time, it also results in the loss of crucial distinguishing information for Hb interval discrimination in the context of non-invasive Hb detection, ultimately leading to suboptimal model performance. Subsequently, this study compares the prediction effects of two prediction frameworks, direct regression and classification-regression two-stage stacking, in terms of using complex network features as inputs. In the comparison of the prediction results, it can be found that if the complex network features and pulse wave morphological features are regressed directly after feature selection, the fitting effect is poor and the model performance results obtained are not optimistic. In contrast, the model framework of classification-regression stacking will achieve better prediction results, of which the best result is SVM-LGBM with an MAE of 6.67 g/L, an RMSE of 8.21 g/L, and an R2 of 0.64. This result can likewise prove that the network features obtained by the complex network technique are more recognizable in interval classification, and thus also prove that when the network technique is applied, the modeling framework of classification regression stacking is superior to direct regression and will achieve better model fit and model accuracy. From the regression results of the model, we can also find that the model performance obtained by using ten features for prediction is better compared to the model performance obtained by twenty and thirty features. This proves that the visibility graph features are of limited help to the regression model and that even the low relevance of the features at the bottom of the feature filtering order can adversely affect the model results. This study also compares the results obtained using the previous prediction framework with the new prediction framework based on the new dataset, and it can be seen that as the expansion of the sample interval is gradually widened, the regression effect of the morphological features using only the PPG signals deteriorates. The overall robustness and stability of the algorithm can be improved, and more accurate performance results can be obtained by using the complex network technique to find the connection between different samples. A comprehensive comparison of the model performance, practicality, robustness, and other metrics is made between this study and other studies using human body part images for Hb detection, multispectral detection, and the method using MW-PPG.