[Rules of acupoint selection and pattern-acupoint relationship in treatment with acupuncture and moxibustion for endometriosis based on complex network analysis technology].

OBJECTIVE: To explore the rules of acupoint selection and pattern-acupoint relationship in treatment with acupuncture and moxibustion for endometriosis (EMs) based on complex network analysis technology. METHODS: The articles for clinical trial of EMs treated with acupuncture and moxibustion were searched from CNKI, Wanfang, VIP, SinoMed, PubMed, EMbase and Cochrane Library from the inception of the databases to December 14, 2022. Using Microsoft Excel 2019 software, the database was established to collect the use frequency of acupoint, meridian tropism, location and pattern-acupoint relationship. SPSS Modeler 18.0 Apriori algorithm was adopted to conduct the association rule analysis, Cytoscape3.7.2 software was used to plot the complex co-occurrence network map; and SPSS Statistics 26.0 was adopted to perform hierarchical cluster analysis on high-frequency acupoints and a tree diagram was drawn. RESULTS: A total of 163 articles were included, and 167 core acupoint prescriptions and 74 pattern-associated acupoint prescriptions were extracted, involving 92 acupoints, with a cumulative frequency of 1 223 times. The top five acupoints with the highest use frequency were Guanyuan (CV 4), Sanyinjiao (SP 6), Zhongji (CV 3), Zigong (EX-CA 1) and Qihai (CV 6). The selected acupoints were mostly distributed in the chest, abdomen and lower limbs; and the involved meridians included the conception vessel, the spleen meridian of foot-taiyin and the stomach meridian of foot-yangming. The acupoint compatibility of high frequency referred to Guanyuan (CV 4) - Sanyinjiao (SP 6), Guanyuan (CV 4) - Zhongji (CV 3), and Guanyuan (CV 4) - Zigong (EX-CA 1). The close association was presented among Guanyuan (CV 4), Sanyinjiao (SP 6), Qihai (CV 6) and Zhongji (CV 3), which had the strongest connection with the other acupoints; among the top 25 acupoints with the highest use frequency, 5 acupoint prescriptions with high frequency were obtained by the cluster analysis. Guanyuan (CV 4), Qihai (CV 6), Sanyinjiao (SP 6), Zigong (EX-CA 1) and Zhongji (CV 3) were selected for cold and blood stagnation; Guanyuan (CV 4), Sanyinjiao (SP 6), Zhongji (CV 3), Dahe (KI 12) and Taixi (KI 3) for kidney deficiency and blood stagnation; Zhongji (CV 3), Guanyuan (CV 4), Sanyinjiao (SP 6), Xuehai (SP 10) and Diji (SP 8) for qi and blood stagnation; Qihai (CV 6), Guanyuan (CV 4), Zusanli (ST 36), Xuehai (SP 10), and Zigong (EX-CA 1) for qi deficiency and blood stagnation; Sanyinjiao (SP 6), Fenglong (ST 40), Zhongliao (BL 33), Ciliao (BL 32) and Xialiao (BL 34) for interaction of phlegm and stasis; and Daheng (SP 15), Guanyuan (CV 4), Zhongji (CV 3), Qihai (CV 6) and Zhongwan (CV 12) for retention of damp and heat. CONCLUSION: The core acupoints are Guanyuan (CV 4), Sanyinjiao (SP 6), Zhongji (CV 3), Qihai (CV 6) and Zigong (EX-CA 1) in treatment of endometriosis with acupuncture and moxibustion. Six patterns/syndromes are involved in clinical practice. In terms of the properties, functions and indications, the supplementary acupoints are selected on the basis of the core acupoints for different patterns/sydnromes of the disease.

AAGCN: a graph convolutional neural network with adaptive feature and topology learning.

In recent years, there has been a growing prevalence of deep learning in various domains, owing to advancements in information technology and computing power. Graph neural network methods within deep learning have shown remarkable capabilities in processing graph-structured data, such as social networks and traffic networks. As a result, they have garnered significant attention from researchers.However, real-world data often face challenges like data sparsity and missing labels, which can hinder the performance and generalization ability of graph convolutional neural networks. To overcome these challenges, our research aims to effectively extract the hidden features and topological information of graph convolutional neural networks. We propose an innovative model called Adaptive Feature and Topology Graph Convolutional Neural Network (AAGCN). By incorporating an adaptive layer, our model preprocesses the data and integrates the hidden features and topological information with the original data's features and structure. These fused features are then utilized in the convolutional layer for training, significantly enhancing the expressive power of graph convolutional neural networks.To evaluate the effectiveness of the adaptive layer in the AAGCN model, we conducted node classification experiments on real datasets. The results validate its ability to address data sparsity and improve the classification performance of graph convolutional neural networks.In conclusion, our research primarily focuses on addressing data sparsity and missing labels in graph convolutional neural networks. The proposed AAGCN model, which incorporates an adaptive layer, effectively extracts hidden features and topological information, thereby enhancing the expressive power and classification performance of these networks.

Adaptive robust structure exploration for complex systems based on model configuration and fusion.

Analyzing and obtaining useful information is challenging when facing a new complex system. Traditional methods often focus on specific structural aspects, such as communities, which may overlook the important features and result in biased conclusions. To address this, this article suggests an adaptive algorithm for exploring complex system structures using a generative model. This method calculates and optimizes node parameters, which can reflect the latent structural characteristics of the complex system. The effectiveness and stability of this method have been demonstrated in comparative experiments on 10 sets of benchmark networks using our model parameter configuration scheme. To enhance adaptability, algorithm fusion strategies were also proposed and tested on two real-world networks. The results indicate that the algorithm can uncover multiple structural features, including clustering, overlapping, and local chaining. This adaptive algorithm provides a promising approach for exploring complex system structures.

Analysis of spatial characteristics and influencing factors of the flow network of highly educated talents from national and local perspective.

Based on dynamic monitoring data on China's population, by using complex networks, spatial analysis and mathematical measurement, this study reveals the spatial characteristics and influencing factors of the network of flows of highly educated talents in the Yangtze River Delta region from the national and local perspectives. In the two perspectives, the network has strong isomorphism and certain differences. The in-flow of highly educated talents from cities with high administrative levels and more developed economies to Shanghai constitutes the core of the entire network. From a national perspective, highly educated talents tend to converge to the Yangtze River Delta region. From a local perspective, it was found that these talents cluster towards a limited number of cities in the region. From both perspectives, the flow network has developed into a "core-periphery" progressive hierarchical structure, with Shanghai becoming the sole core city. There is little difference in the influencing factors of talent mobility from both macro and meso perspectives. Highly educated talents would frequently flow between cities with strong economic development levels, and cities with high education level, scientific and technological level, complete infrastructure, and good aesthetics. However, geographical distance still plays a hindering role in the flow of highly educated talents, and factors such as cultural identity, institutional, and social modality differences among regions also have a certain effect on the flow of these talents.

Dynamical Analysis of an Improved Bidirectional Immunization SIR Model in Complex Network.

In order to investigate the impact of two immunization strategies-vaccination targeting susceptible individuals to reduce their infection rate and clinical medical interventions targeting infected individuals to enhance their recovery rate-on the spread of infectious diseases in complex networks, this study proposes a bilinear SIR infectious disease model that considers bidirectional immunization. By analyzing the conditions for the existence of endemic equilibrium points, we derive the basic reproduction numbers and outbreak thresholds for both homogeneous and heterogeneous networks. The epidemic model is then reconstructed and extensively analyzed using continuous-time Markov chain (CTMC) methods. This analysis includes the investigation of transition probabilities, transition rate matrices, steady-state distributions, and the transition probability matrix based on the embedded chain. In numerical simulations, a notable concordance exists between the outcomes of CTMC and mean-field (MF) simulations, thereby substantiating the efficacy of the CTMC model. Moreover, the CTMC-based model adeptly captures the inherent stochastic fluctuation in the disease transmission, which is consistent with the mathematical properties of Markov chains. We further analyze the relationship between the system's steady-state infection density and the immunization rate through MCS. The results suggest that the infection density decreases with an increase in the immunization rate among susceptible individuals. The current research results will enhance our understanding of infectious disease transmission patterns in real-world scenarios, providing valuable theoretical insights for the development of epidemic prevention and control strategies.

Variability of large timescale functional networks in patients with disorders of consciousness.

Objective: Most brain function assessments for disorders of consciousness (DOC) utilized quantified characteristics, measured only once, ignoring the variation of patients' brain states. The study aims to investigate the brain activities of patients with DOC from a new perspective: variability of a large timescale functional network. Methods: Forty-nine patients were enrolled in this study and performed a 1-week behavioral assessment. Subsequently, each patient received electroencephalography (EEG) recordings five times daily at 2-h intervals. Functional connectivity and networks were measured by weighted phase lag index and complex network parameters (characteristic path length, cluster coefficient, and betweenness centrality). The relative coefficient of variation (CV) of network parameters was measured to evaluate functional network variability. Results: Functional networks of patients with vegetative state/unresponsive wakefulness syndrome (VS/UWS) showed significantly higher segregation (characteristic path length) and lower centrality (betweenness centrality) than emerging from the minimal conscious state (EMCS) and minimal conscious state (MCS), as well as lower integration (cluster coefficient) than MCS. The functional networks of VS/UWS patients consistently presented the highest variability in segregation and integration (i.e., highest CV values of characteristic path length and cluster coefficient) on a larger time scale than MCS and EMCS. Moreover, the CV values of characteristic path length and cluster coefficient showed a significant inverse correlation with the Coma Recovery Scale-Revised scores (CRS-R). The CV values of network betweenness centrality, particularly of the cento-parietal region, showed a positive correlation with the CRS-R. Conclusion: The functional networks of VS/UWS patients present the most invariant segregation and integration but divergent centrality on the large time scale networks than MCS and EMCS. Significance: The variations observed within large timescale functional networks significantly correlate with the degree of consciousness impairment. This finding augments our understanding of the neurophysiological mechanisms underpinning disorders of consciousness.

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Building a scientific and reasonable ecological network is the key for optimizing the pattern of territorial development and protection, and is of great significance for ensuring regional ecological security and promoting the virtuous cycle of ecosystems. In previous studies, nodal attack method (destruction of ecological source area) was often used in the "robustness" evaluation of ecological networks. Actually, the ecological corridor is more fragile than the source area, and thus the nodal attack method is not reasonable. In this study, taking Jiuquan City as the research area, based on the circuit model to construct the ecological network, we carried out the topology optimization of ecological network by using three strategies (random edge increase, node degree and priority edge increase with low node intermedium number) in complex network theory. We compared and analyzed the "robustness" of ecological network before and after optimization by constructing edge attack strategy, and selected the best network optimization strategy. The results showed that 65 ecological source areas were identified in Jiuquan City, with a total area of 20275.15 km2, and that grassland accounted for 89.5% of the source area. We identified 179 ecological corridors with a total length of 6387.16 km, 158 ecological barrier points with a total area of 1385.5 km2. The unused land accounted for 92.2% of the total barrier points area. We identified 63 ecological pinch points, mainly concentrated in the source edge and corridor intersection. Among them, the spatial distribution of 11 barrier points and pinch points was consistent, which was the key area to be repaired in ecological network optimization. The three optimization strategies had significantly improved the stability of ecological network in Jiuquan City. The relative size of the maximum connected subgraph and the edge connected rate of the ecological network of the optimization strategy of adding edges according to degree were all the most stable under random attack mode and deliberate attack mode, which was the best optimization scheme for ecological network in Jiuquan City.

On propagation in networks, promising models beyond network diffusion to describe degenerative brain diseases and traumatic brain injuries.

Introduction: Connections among neurons form one of the most amazing and effective network in nature. At higher level, also the functional structures of the brain is organized as a network. It is therefore natural to use modern techniques of network analysis to describe the structures of networks in the brain. Many studies have been conducted in this area, showing that the structure of the neuronal network is complex, with a small-world topology, modularity and the presence of hubs. Other studies have been conducted to investigate the dynamical processes occurring in brain networks, analyzing local and large-scale network dynamics. Recently, network diffusion dynamics have been proposed as a model for the progression of brain degenerative diseases and for traumatic brain injuries. Methods: In this paper, the dynamics of network diffusion is re-examined and reaction-diffusion models on networks is introduced in order to better describe the degenerative dynamics in the brain. Results: Numerical simulations of the dynamics of injuries in the brain connectome are presented. Different choices of reaction term and initial condition provide very different phenomenologies, showing how network propagation models are highly flexible. Discussion: The uniqueness of this research lies in the fact that it is the first time that reaction-diffusion dynamics have been applied to the connectome to model the evolution of neurodegenerative diseases or traumatic brain injury. In addition, the generality of these models allows the introduction of non-constant diffusion and different reaction terms with non-constant parameters, allowing a more precise definition of the pathology to be studied.

Uncovering the complexity of childhood undernutrition through strain-level analysis of the gut microbiome.

BACKGROUND: Undernutrition (UN) is a critical public health issue that threatens the lives of children under five in developing countries. While evidence indicates the crucial role of the gut microbiome (GM) in UN pathogenesis, the strain-level inspection and bacterial co-occurrence network investigation in the GM of UN children are lacking. RESULTS: This study examines the strain compositions of the GM in 61 undernutrition patients (UN group) and 36 healthy children (HC group) and explores the topological features of GM co-occurrence networks using a complex network strategy. The strain-level annotation reveals that the differentially enriched species between the UN and HC groups are due to discriminated strain compositions. For example, Prevotella copri is mainly composed of P. copri ASM1680343v1 and P. copri ASM345920v1 in the HC group, but it is composed of P. copri ASM346549v1 and P. copri ASM347465v1 in the UN group. In addition, the UN-risk model constructed at the strain level demonstrates higher accuracy (AUC = 0.810) than that at the species level (AUC = 0.743). With complex network analysis, we further discovered that the UN group had a more complex GM co-occurrence network, with more hub bacteria and a higher clustering coefficient but lower information transfer efficiencies. Moreover, the results at the strain level suggested the inaccurate and even false conclusions obtained from species level analysis. CONCLUSIONS: Overall, this study highlights the importance of examining the GM at the strain level and investigating bacterial co-occurrence networks to advance our knowledge of UN pathogenesis.

Employing graph attention networks to decode psycho-metabolic interactions in Schizophrenia.

Schizophrenia is a severe mental disorder characterized by intricate and underexplored interactions between psychological symptoms and metabolic health, presenting challenges in understanding the disease mechanisms and designing effective treatment strategies. To delve deeply into the complex interactions between mental and metabolic health in patients with schizophrenia, this study constructed a psycho-metabolic interaction network and optimized the Graph Attention Network (GAT). This approach reveals complex data patterns that traditional statistical analyses fail to capture. The results show that weight management and medication management play a central role in the interplay between psychiatric disorders and metabolic health. Furthermore, additional analysis revealed significant correlations between the history of psychiatric symptoms and physical health indicators, as well as the key roles of biochemical markers(e.g., triglycerides and low-density lipoprotein cholesterol), which have not been sufficiently emphasized in previous studies. This highlights the importance of medication management approaches, weight management, psychological treatment, and biomarker monitoring in comprehensive treatment and underscores the significance of the biopsychosocial model. This study is the first to utilize a GNN to explore the interactions between schizophrenia symptoms and metabolic features, providing new insights into understanding psychiatric disorders and guiding the development of more comprehensive treatment strategies for schizophrenia.

Assessment and solutions for vulnerability of urban rail transit network based on complex network theory: A case study of Chongqing.

As a typical complex network system, the operating environment of rail transit network (RTN) is complex and demanding. This study aims to accurate assess the weaknesses and vulnerability of RTN, which is crucial for ensuring its smooth operation. Taking Chongqing Rail Transit (CRT) as an example, this study developed a network topology model using the spatial L method and analyzed the network structure characteristics, along with the importance of key nodes under different indicators, based on complex network theory. Additionally, this study analyzed the geographical spatial distribution characteristics of nodes based on the topography and urban spatial structure of Chongqing. Then, this study classified the nodes in the RTN according to basic topological indicators, namely degree, betweenness centrality, network efficiency, and passenger flow volume (PFV). The results indicated six cluster of nodes, reflecting the variability in node vulnerability concerning overall influence (providing alternative paths, reducing path length), regional aggregation capacity, and transportation capacity. Finally, this study proposed targeted management strategies for different clusters of nodes and their respective geographical locations, providing necessary references for rational planning, safety protection, and sustainable construction of RTN.

Integrated analysis and separation of 5-lipoxygenase inhibitors from triterpenes of Poria cocos using bioaffinity ultrafiltration UPLC-Q-Exactive, molecular docking and target-based multiple complex networks.

Poria cocos (Schw.) Wolf (P. cocos) has been widely used as medical plant in East Asia with remarkable anti-Alzheimer's disease (anti-AD) activity. However, the underlying mechanisms are still confused. In this study, based on the ß-Amyloid deposition hypothesis of AD, an integrated analysis was conducted to screen and separation 5-lipoxygenase (5-LOX) inhibitors from triterpenoids of P. cocos and investigate the anti-AD mechanisms, containing bioaffinity ultrafiltration UPLC-Q-Exactive, molecular docking, and multiple complex networks. Five triterpenoids were identified as potential 5-LOX inhibitors, including Tumulosic acid, Polyporenic acid C, 3-Epi-dehydrotumulosic acid, Pachymic acid and Dehydrotrametenolic acid. Five potential 5-LOX inhibitors were screened by ultrafiltration affinity assay in P. cocos. The molecular docking simulation results are consistent with the ultrafiltration experimental results, which further verifies the accuracy of the experiment. The commercial 5-LOX inhibitor that Zileuton was used as a positive control to evaluate the inhibitory effect of active ingredients on 5-LOX. Subsequently, the established separation method allowed the five active ingredients (Pachymic acid, 3-Epi-dehydrotumulosic acid, Dehydrotrametenolic acid, Tumulosic acid and Polyporenic acid C) with high purity to be isolated. Targeting network pharmacology analysis showed that five active ingredients correspond to a total of 286 targets. Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis found that target cells were mainly enriched in Pathways in cancer, Lipid and atherosclerosis. Our results indicate that P. cocos extract has the potential to be used in the prevention and treatment of neurodegenerative diseases. This will help elucidate the mechanisms of action of various medicinal plants at the molecular level and provide more opportunities for the discovery and development of new potential treatments from health food resources.

A Spatial Artificial Chemistry Implementation of a Gene Regulatory Network Aimed at Generating Protein Concentration Dynamics.

Gene regulatory networks are networks of interactions in organisms responsible for determining the production levels of proteins and peptides. Mathematical and computational models of gene regulatory networks have been proposed, some of them rather abstract and called artificial regulatory networks. In this contribution, a spatial model for gene regulatory networks is proposed that is biologically more realistic and incorporates an artificial chemistry to realize the interaction between regulatory proteins called the transcription factors and the regulatory sites of simulated genes. The result is a system that is quite robust while able to produce complex dynamics similar to what can be observed in nature. Here an analysis of the impact of the initial states of the system on the produced dynamics is performed, showing that such models are evolvable and can be directed toward producing desired protein dynamics.

A review of graph and complex network theory in water distribution networks: Mathematical foundation, application and prospects.

Graph theory (GT) and complex network theory play an increasingly important role in the design, operation, and management of water distribution networks (WDNs) and these tasks were originally often heavily dependent on hydraulic models. Facing the general reality of the lack of high-precision hydraulic models in water utilities, GT has become a promising surrogate or assistive technology. However, there is a lack of a systematic review of how and where the GT techniques are applied to the field of WDNs, along with an examination of potential directions that GT can contribute to addressing WDNs' challenges. This paper presents such a review and first summarizes the graph construction methods and topological properties of WDNs, which are mathematical foundations for the application of GT in WDNs. Then, main application areas, including state estimation, performance evaluation, partitioning, optimal design, optimal sensor placement, critical components identification, and interdependent networks analysis, are identified and reviewed. GT techniques can provide acceptable results and valuable insights while having a low computational burden compared with hydraulic models. Combining GT with hydraulic model significantly enhances the performance of analysis methods. Four research challenges, namely reasonable abstraction, data availability, tailored topological indicators, and integration with Graph Neural Networks (GNNs), have been identified as key areas for advancing the application and implementation of GT in WDNs. This paper would have a positive impact on promoting the use of GT for optimal design and sustainable management of WDNs.

The effect of incentive policies on the diffusion of construction and demolition waste recycling: A government perspective.

Construction and demolition (C&D) waste recycling plays a significant role in waste reduction and carbon reduction, which is critical for sustainable development. However, due to various limitations such as financial problems, C&D waste recycling industry is not well developed in developing countries. To address this problem, this study combines complex network theory and evolutionary game theory to analyse the diffusion of C&D waste recycling behaviour among enterprises under governmental incentive policies within a complex network context. The results demonstrate that the size of the network has limited effects on behaviour diffusion in Watts-Strogatz small-world network. Additionally, the study highlights the clear impact of governmental incentive probability, initial rate and connection degree on the diffusion path. By quantitatively investigating the effects of incentive tools, this study contributes to the knowledge of C&D waste management and provides valuable implications for stakeholders seeking to promote the diffusion of C&D waste recycling.

Coexistence mechanism of ecological specialists and generalists based on a network dimension reduction method.

As an ecological strategy for species coexistence, some species adapt to a wide range of habitats, while others specialize in particular environments. Such 'generalists' and 'specialists' achieve normal ecological balance through a complex network of interactions between species. However, the role of these interactions in maintaining the coexistence of generalist and specialist species has not been elucidated within a general theoretical framework. Here, we analyze the ecological mechanism for the coexistence of specialist and generalist species in a class of mutualistic and competitive interaction ecosystems based on the network dimension reduction method. We find that ecological specialists and generalists can be identified based on the number of their respective interactions. We also find, using real-world empirical network simulations, that the removal of ecological generalists can lead to the collapse of local ecosystems, which is rarely observed with the loss of ecological specialists.

Research on regional ozone prevention and control strategies in eastern China based on pollutant transport network and FNR.

O3 pollution in China has worsened sharply in recent years, and O3 formation sensitivity (OFS) in many regions have gradually changed, with eastern China as the most typical region. This study constructed the transport networks of O3 and NO2 in different seasons from 2017 to 2020. The transport trends and the clustering formation patterns were summarized by analyzing the topological characteristics of the transport networks, and the patterns of OFS changes were diagnosed by analyzing the satellite remote sensing data. Based on that, the main clusters that each province or city belongs to in different pollutant transport networks were summarized and proposals for the inter-regional joint prevention and control were put forward. As the results showed, O3 transport activity was most active in spring and summer and least active in winter, while NO2 transport activity was most active in autumn and winter and least active in summer. OFS in summer mainly consisted of transitional regimes and NOx-limited regimes, while that in other seasons was mainly VOC-limited regimes. Notably, there was a significant upward trend in the proportion of transitional regimes and NOx-limited regimes in spring, autumn, and winter. For regions showing NOx-limited regime, areas with higher out-weighted degrees in the NO2 transport network should focus on controlling local NOx emissions, such as central regions in summer. For regions showing VOC-limited regime, areas with higher out-weighted degrees in the O3 transport network should focus on controlling local VOCs emissions, such as central and south-central regions in summer. For regions that belong to the same cluster and present the same OFS in each specific season, regional cooperative emission reduction strategies should be established to block important transmission paths and weaken regional pollution consistency.

Comorbidity risk and distribution characteristics of chronic diseases in the elderly population in China.

BACKGROUND: The risk of comorbid chronic diseases in elderly people is an important problem affecting their health and quality of life. We analyzed the incidence of chronic diseases for combinations of chronic diseases analyzed. METHODS: We used the original data to construct hypothetical cohorts of elderly individuals that evolved with age. The complex network was used to reduce the dimensionality of disease. The multistate transition model is used to calculate the incidence of each chronic disease, exploring comorbidity characteristics and rules. RESULTS: (1) By using complex network, seven chronic diseases were screened out in men, including hypertension, diabetes, heart disease, stroke, chronic lung disease, arthritis and dyslipidemia; six chronic diseases in women showed significant comorbidity except chronic lung disease. (2) Incidence show differences in age and sex; incidence of chronic diseases generally increased with age. (3) The marginal risk increases with the number of basic chronic diseases associated with comorbidities. (4) When hypertension is present as a basic disease, its impact on the risk of other chronic diseases is much less than that of other chronic diseases. (5) When diseases occur as basic chronic diseases, hypertension-heart disease and diabetes-dyslipidemia are combinations that have the greatest impact on each other in men; hypertension-heart disease in women. CONCLUSIONS: The incidence of chronic diseases in patients who have chronic diseases and will form comorbidities differs from that in healthy states, and the related effects of different chronic diseases also differ. Among these conditions, hypertension is caused by a special mechanism.

Evolution of global value-added trade networks and response to risk transmission.

To enhance the international division of labor of developing countries and scientifically respond to the risks and conflicts in the network system, it is important to examine the evolutionary characteristics of the global value-added trade network, simulate the impact of risk shocks, and propose corresponding measures. Based on UNCTAD-Eora value-added trade data, this paper measured and evaluated the evolution characteristics of the global value-added trade network from 2003 to 2018 using the social network analysis and value-added decomposition methods. Then we analyze the impact of risk shocks on the evolution of the trade network using the bootstrap percolation model, building global trade networks and proposing countermeasures. The results show that the global value-added trade network has formed a complex structure and structurally stable distribution pattern, with Germany, China, and the U.S. as the core and the most crucial supports. Among which, China's core position is mainly due to the rapid rise in its export center status. The trade benefits of the three core countries are both competitive and complementary along the "One Belt and One Road". Furthermore, simulations of bootstrap percolation model reveals that the adoption of trade protection policies (caused by poor institutional quality) by different countries will spread and diffuse non-linearly in the network, and the impacts triggered by low-centered countries are comparatively more widespread. By improving defense capabilities and changing the network structure, the "cascading" impacts of trade policy uncertainty can be reduced.