The new energy vehicle industry is of great significance to the implementation of the “dual-carbon” policy in China. To identify the risk spillover mechanism of China’s new energy automobile industry chain under crisis events, DCC-GJR-GARCH-MES model, complex network analysis and interpretable machine learning are introduced to identify the tail risk spillover mechanism and time-varying drivers of new energy automobile firms from the industry chain perspective. It is found that: (1) The risk spillover of enterprises in different links of the industrial chain is heterogeneous, with upstream enterprises having higher systemic risk than midstream and downstream enterprises. (2) Different links in the industrial chain, risk transmission paths, and core transmission nodes vary across crisis events. (3) Risk drivers in the industrial chain exhibit significant time-varying characteristics. During the entire sample period, enterprise size, interconnectedness, and investor attention were the key drivers of systemic importance. The conclusion of the study reveals the unique vulnerability and coping mechanism of each link in China's new energy automobile industry chain in the face of external shocks, which provides an important basis for further optimizing the risk management strategy of the new energy automobile industry chain.

Abstract North China frequently experiences devastating extreme precipitation events (EPEs). Upstream spatiotemporal propagation characteristics of EPEs can provide useful precursors for forecasting North China EPEs but remains poorly understood. Using climate network analysis, two dominant EPEs propagation pathways, that is, a northwestern pathway from West Siberian Plain (6‐day lead) and a southwestern pathway from Tibetan Plateau (TP) (3‐day lead), are identified during the warm season. The northwestern pathway is driven by an eastward‐propagating mid‐latitude wave train coupled with Arctic cyclone. The cyclone in the wave train drives the southeastward‐propagating EPEs, and when it merges with the Arctic cyclone, its downstream anticyclone is enhanced, ultimately inducing North China EPEs. The southwestern pathway stems from quasi‐stationary waves coupled with Arctic anticyclone. Interactions between anticyclone in the wave train and Arctic anticyclone generate a TP cyclone, whose intensification and eastward expansion propel EPEs northeastward and strengthen the Northeast Asian anticyclone, ultimately causing North China EPEs.

Abstract Link prediction is a crucial task in complex network analysis, aiming to predict future connections between nodes in a graph. This problem is particularly challenging in sparse networks, where the low number of edges complicates traditional prediction methods. To address this, we propose singular value decomposition-graph attention network-gradient boosting (SVD-GAT-GB), a hybrid approach that densifies the sparse graph using truncated SVD, improves node representations through a GAT, and applies GB for accurate link prediction. Our method effectively handles the sparsity issue, significantly enhancing the prediction accuracy by leveraging structural information extracted from the densified graph. We validate our approach through extensive experiments on multiple datasets, demonstrating improvements in both F1-score and AUC by approximately 32% and 11%, respectively, over existing methods. The proposed model proves to be accurate and robust across diverse types of networks, making it an effective solution for link prediction in sparse graphs.

Complex network analysis of mobility dynamics in Seoul during the COVID-19 pandemic 2020–2022

The unbalanced development of production-living-ecological land (PLEL) on the northern slope of the Tianshan Mountains has led to a series of environmental and ecological problems. Clarifying the evolution of spatial and temporal patterns and driving mechanisms of the PLEL is highly important for promoting the optimization of land use functions and sustainable development in this region. Previous research has focused primarily on the area or probability of conversion between different types of PLEL, neglecting the overall structural characteristics of the PLEL system. It is difficult to quantify the connectivity and importance of each PLEL type within the entire PLEL system, making it challenging to identify key PLEL types. Furthermore, quantitative characterization of the PLEL system stability is lacking. Accordingly, in this paper, PLEL conversion networks were constructed on the basis of complex network theory, and the dynamic evolution of the PLEL was analyzed from a systemic and holistic perspective. Network metrics (weighted degree, integrated node centrality, and average path length) were calculated to identify key types of PLEL, analyze the main conversion processes of PLEL, and quantify the stability of the PLEL system. The results indicated that: (1) In the PLEL system, ecological land occupied a dominant position but gradually declined between 2000 and 2023. (2) The grassland ecological land, agricultural production land, and ranching production land all had high integrated node centrality and were identified as key types in the PLEL conversion network from 2000 to 2023. (3) The conversion of grassland ecological land, ecological accommodation land, and ranching production land into agricultural production land were the main process of PLEL conversion, with conversion ratios of 53%, 28%, and 16%, respectively. (4) The average path length of the PLEL conversion network from 2000 to 2023 was 1.153, indicating that the overall stability of the PLEL system was poor and that the conversion between PLEL types was easy. (5) The PLEL evolution was the combined result of natural, economic, and social factors. This study demonstrated that complex network models can effectively identify key regulatory land types within the PLEL system. Furthermore, the system's high instability serves as a warning that the current PLEL development model is unsustainable. This insight provides a crucial scientific basis for precise spatial management and ecological security safeguards on the northern slope of the Tianshan Mountains.

Designing a traditional village cluster protection-utilization system via complex network analysis: Qiandongnan case study

ABSTRACT Embodied pesticide transfers through global trade networks present complex transboundary challenges, causing ecological degradation in producing regions and consumption risks worldwide. This study employs an integrated multi-regional input-output (MRIO) and complex network analysis framework, highlighting an innovative matching method for dynamic community detection to map the structural dynamics of embodied pesticide flows across 175 economies from 1992 to 2021, and draws the following key findings. Firstly, the volume of embodied pesticides has risen significantly over time and is concentrated in agricultural sectors and intermediate goods, with pronounced geographic asymmetry. Secondly, different pesticide categories show distinct net trade flows, with a few economies, such as the U.S.A., Brazil, and China, shifting roles over time, contributing to the majority of main global trade and dominating the topological dynamics of the network. Thirdly, embodied pesticide networks have short average path lengths with small-world features weakening, indicating highly direct and more random interconnectivity which supports the process of trade liberalization. Finally, the formation and evolution of communities within trade networks are shaped by economic, geographical, and political factors, with major communities centred on core economies – such as China and the United States – playing crucial roles as key importers and exporters, respectively, since around 2006.

ABSTRACTRecording and quantifying ecological interactions is vital for understanding biodiversity, ecosystem stability, and resilience. Camera traps have become a key tool for documenting plant–animal interactions, especially when combined with computer vision (CV) technology to handle large datasets. However, creating comprehensive ecological interaction databases remains challenging due to labor‐intensive processes and a lack of standardization. While CV aids in data processing, it has limitations, including information loss, which can impact subsequent analyses. This study presents a detailed methodology to streamline the creation of robust ecological interaction databases using CV‐enhanced tools. It highlights potential pitfalls in applying CV models across different contexts, particularly for specific plant and animal species. The approach aligns with existing camera trap standards and incorporates complex network analysis tools. It also addresses a gap in ecological research by extending the methodology to behavioral studies using video‐based image recognition, as most current studies rely on still images. The study evaluates CV's performance in estimating species interaction frequency (PIE) and its ecological implications. Results show that up to 10% of pairwise interactions may be missed with CV, with information loss varying among focal species and individual plants. The loss of information is minimal compared to the vast data CV enables researchers to gather especially if data is intended to be used in community‐level approaches where only three out of 344 unique pairwise interactions were missed. In community‐level approaches, the overall estimates of both PIEs and interaction strengths remained largely unaffected. The methodology provides a valuable resource for ecologists seeking to document ecological interactions efficiently. It offers guidelines for collecting reliable data while addressing CV's limitations in capturing unbiased species interaction data. Despite its constraints, CV significantly enhances the ability to gather large‐scale interaction data, particularly at the community level, making it an indispensable tool for ecological research.

To improve the safety of road transportation of Spent Nuclear Fuel (SNF), this paper proposes a novel approach for risk identification and chaotic synchronous control in SNF road transportation systems. Firstly, a dynamic risk evolution model for the road transportation of SNF is developed by analyzing the nonlinear interactions among vehicles, environmental conditions, and human factors using complex network analysis and nonlinear dynamics. Secondly, an enhanced K-shell decomposition method is applied to identify key risk nodes and assess the relative importance of different risk factors, providing a basis for targeted risk control. Finally, a chaotic synchronization control strategy based on Lyapunov stability is proposed to suppress risk divergence and restore system stability. Three targeted control schemes are evaluated by varying the control gain coefficients across the ‘Vehicle–Environment–Human’ dimensions. Simulation results indicate that the strategy prioritizing environmental and human risk control yields the fastest convergence, significantly outperforming vehicle-centric approaches. The results show that prioritizing both environmental and human-factor control is most effective for suppressing chaotic divergence. This provides a solid quantitative basis for the strategic shift from passive defense to active environmental warning, thereby significantly optimizing the dynamic risk management of the SNF transportation system.

Heatwaves pose increasing risks to human health and socio-economic systems, yet their spatiotemporal organization and underlying synergistic mechanisms remain insufficiently understood, particularly with respect to daytime and nighttime processes. Using a dual identification framework combining absolute and relative temperature thresholds, this study systematically investigates the spatiotemporal evolution of daytime and nighttime heatwaves across China during 1961–2022. A complex network approach is further introduced to characterize the interannual co-variability and interdecadal structural evolution of heatwave activity from a system-level perspective. Results reveal a pronounced interdecadal transition in the early 1990s, accompanied by a fundamental reorganization of heatwave co-occurrence networks. Heatwave frequency exhibits a clear post-transition desynchronization, characterized by a sharp decline in network connectivity and fragmented local clustering, indicating a shift from large-scale, circulation-dominated coherence toward increasingly localized and heterogeneous heatwave occurrences. In contrast, heatwave duration shows an opposite evolution, with significantly enhanced spatial synchronization after the transition. Degree centrality and clustering coefficients increase markedly, and high-connectivity cores expand from coastal regions into inland areas, including North, Central, and Northwest China. This coexistence of desynchronized heatwave occurrence and strongly synchronized persistence suggests an emerging high-risk regime in which heatwaves occur more randomly but, once initiated, tend to persist coherently across large regions. Furthermore, a dual-layer network analysis reveals previously undocumented cross-temporal coupling between daytime and nighttime heatwaves, with pronounced regional differences. The middle and lower reaches of the Yangtze River are more strongly influenced by local processes, whereas northern China is increasingly governed by large-scale circulation control and enhanced regional clustering after the transition. These findings demonstrate that complex network analysis provides a powerful framework for uncovering hidden structural changes in extreme heat events and offer new insights into the evolving risks of compound and persistent heatwaves under climate change.

To explore the acupoint compatibility patterns of acupuncture for ovarian function decline-related diseases using complex network technology, and to analyze the mechanism of core acupoints through network acupuncture methods. Literature regarding acupuncture for ovarian function decline-related diseases was retrieved from CNKI, Wanfang, VIP, SinoMed, PubMed, EMbase, Cochrane Library, and Web of Science databases from their inception to April 8th, 2024. Frequency analysis, association rule analysis, and complex network analysis were conducted on the selected acupoints to identify core acupoint prescriptions. Core acupoint targets were analyzed from the retrieved literature, and targets for ovarian function decline-related diseases were identified using GeneCards, Therapeutic Target, and DisGeNET databases. Protein-protein interaction (PPI) networks were constructed for intersecting targets using the STRING database. gene ontology (GO) enrichment analysis and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis were performed using R software. A "core acupoint-target-pathway" network diagram of acupuncture for ovarian function decline-related diseases was constructed using Cytoscape software. A total of 133 studies were included, extracting 186 acupoint prescriptions involving 101 acupoints. The core prescription consisted of 15 acupoints, and 73 acupoint targets were identified, involving 13 core acupoints, including Guanyuan (CV4), Sanyinjiao (SP6), Shenshu (BL23), Zhongwan (CV12), Zusanli (ST36), Zhongji (CV3), Qihai (CV6), Baihui (GV20), Taichong (LR3), Taixi (KI3), Ciliao (BL32), Zigong (EX-CA1), and Guilai (ST29). There were 40 intersecting targets between the core acupoints and ovarian function decline-related diseases, with key targets including tumor protein 53, protein kinase B alpha, interleukin 1 beta, interleukin 6, interleukin 10, tumor necrosis factor, and nuclear factor kappa B subunit 1. These targets were associated with multiple signaling pathways such as T-helper 17 (Th17) cell differentiation, inflammatory bowel disease, hepatitis B, influenza A, and human immunodeficiency virus 1 (HIV-1) infection, regulating biological processes including apoptosis, inflammatory response, and immune response. The core acupoints of acupuncture for ovarian function decline-related diseases exert therapeutic effects by acting on multiple targets and pathways, thereby delaying ovarian aging. This provides a theoretical basis for the mechanism research of acupoint prescriptions.

The dynamic cross-transmission characteristics of air pollution between cities were explored based on the data of AQI， PM2.5， and other important pollutant concentrations and meteorological indicators in the Beijing-Tianjin-Hebei urban agglomeration from 2017 to 2023， using a Copula function-based feature selection model to identify the key features of composite atmospheric pollution， constructing a Bayesian Extreme Gradient Boosting （BOA-XGBoost） model for dynamic prediction of composite atmospheric pollution in urban agglomeration， and combining complex network analysis and SHAP interpretable modeling. The results show that： ① The feature selection model based on Copula function successfully identified the atmospheric pollutants that were highly correlated with the AQI as PM2.5， PM10， O3， and NO2 and meteorological factors as the average temperature and average humidity.② Compared with other models， the BOA-XGBoost model performed optimally under the comprehensive evaluation system， in which the R2， MAE， RMSE， and MAPE of the Beijing test set reached 0.970 4， 3.965 5， 8.351 8， and 0.062 4， respectively. ③ The pollution transmission network of the Beijing-Tianjin-Hebei urban agglomeration has gradually evolved into a "core-periphery" model， with Tianjin and Cangzhou as the core nodes of pollution transmission weakening and Zhangjiakou， Chengde， and Qinhuangdao being in the periphery for a long time. ④ There was heterogeneity in the strength of pollution transport between urban subgroups， but pollution transport was weakened within them. Pollutant contributions showed that PM2.5 and PM10 dominated the AQI changes in the subgroups， followed by O3， while other pollutants had small and complex effects. ⑤ The city cluster mainly consisted of "double-high" and "double-low" cities， with Beijing switching from "double-high" to "sink" in 2020. The "double-high" cities such as Tianjin and Shijiazhuang will remain the center of pollution transmission.

On the Medication Rules in Shang Han Lun Based on Complex Network Analysis

Complex network analysis of water scarcity risk propagation in the Yellow River Basin under the quality–quantity–environmental flow requirement (QQE) framework

Automated negotiation in multi-agent electronic commerce environments relies heavily on efficient and reliable matching mechanisms to connect negotiation participants. However, existing matching protocols often fail to ensure transaction security and user data privacy, while also lacking adaptability to dynamic negotiation contexts. To address these challenges, this study proposes a privacy-enhanced multi-agent matching optimization framework that integrates trust evaluation, privacy protection, and adaptive decision-making. First, a trust-based negotiation relationship network is constructed through complex network analysis to establish a secure and trustworthy negotiation environment. Second, a privacy-enhanced automated negotiation protocol is developed, employing the cumulative distribution function to transform sensitive data into probabilistic representations, thereby safeguarding user privacy without compromising data availability. Finally, a reinforcement learning algorithm is incorporated to optimize the matching process dynamically, using satisfaction as the reward function to achieve efficient and Pareto-optimal results. A series of experiments verify the framework’s effectiveness, demonstrating significant improvements in system robustness, adaptability, and matching accuracy. This study aims to provide a comprehensive solution that integrates trust network modeling, privacy protection, and adaptive matching optimization, serving as a valuable reference for the development of secure and intelligent automated negotiation platforms.

The resilience and vulnerability of the Maritime Silk Road transportation network (MSRTN) have become critical issues in the face of external disruptions and emergencies. To address these challenges, this study develops a comprehensive evaluation framework grounded in complex network theory, aiming to systematically investigate the structural and functional attributes of the MSRTN. Initially, by integrating socio-geographical data, an optimized clustering algorithm is employed to measure the connectivity strength among ports across diverse regions, forming a robust socio-spatial network. Then, the fuzzy comprehensive evaluation method is applied to evaluate the competitive potential of individual ports. Subsequently, a gravity model is utilized to simulate the interactions and flow dynamics within the MSRTN. Finally, advanced complex network analysis techniques are implemented to uncover the network’s topological properties and resilience patterns. The results in the defined scenario application demonstrate that the MSRTN exhibits high accessibility and distinct small-world characteristics. In addition, the network displays significant resilience when exposed to external risks, only transitioning to a vulnerable state under sustained pressure, validating the robustness of the proposed model. This study provides critical insights into the operational dynamics of the MSRTN and offers strategic recommendations for fostering sustainable growth and resilience in ports along the Maritime Silk Road.

Global climate change results in increasing challenges to the structural security of China’s food system, while pronounced spatial heterogeneities in provincial production and consumption intensify the risk of supply-demand imbalance. This study examines the resilience of China’s inter-provincial staple food flow network from a systemic perspective and identifies its key drivers. Inter-provincial food flows are first inferred using a cost-minimization optimization model. Network resilience is then evaluated by integrating complex network analysis with ecological network resilience theory. Finally, econometric analysis is applied to quantify the relative contributions of multiple structural factors to resilience dynamics. The results reveal an overall decline in the resilience of aggregated staple food, alongside persistently low resilience in soybeans network, indicating heightened structural vulnerability. Substantial heterogeneity is observed across staples in both resilience levels and underlying mechanisms. In general, greater connectivity and diversity of flow paths enhance system resilience, although this effect is markedly weaker for soybeans due to concentrated and import-dependent supply structures. By explicitly linking flow, network structure, and resilience, this study provides system-level insights into the functioning of inter-provincial food flow networks. The proposed analytical framework offers a transferable tool for assessing interregional food flow resilience and supports evidence-based strategies for validating system robustness under uncertainties.

Driven by comparative returns, non-grain use of cultivated land (NGUCL) has intensified, posing risks to food security. This study approaches the problem by employing a risk transfer valuation framework, integrating a multi-regional input–output model with a synthetic risk index to establish China’s virtual cultivated land risk transfer network. Complex network analysis was utilized to explore its features while a temporal exponential random graph model was used to identify driving factors of its formation. Results indicate that fewer provinces took on additional pressures and risks. Despite differing motifs, transfer patterns showed little variation. Block analysis showed increasing net recipient relationships (from four to nine) and variable block divisions. Economic development and industrial structure are negatively associated with outgoing transfers, whereas population, production capacity and resource endowment are positively associated with them. The network exhibits time-dependent stability, with few new risk transfer paths forming. This study provides a theoretical basis and new ideas for optimizing land resource efficiency, re-shaping risk transfer patterns and maintaining food security.

The rapid growth of electric vehicles has intensified the spatial mismatch between the layout of charging infrastructure and user demand, resulting in a structural contradiction in which “local oversupply” and “local shortages” coexist. To systematically diagnose and optimize this issue, this study develops an innovative analytical framework for a “residential area–charging infrastructure” collaborative service network and conducts an empirical analysis using Hongshan District in Wuhan as a case study. The framework integrates actual facility utilization data, complex network analysis, and spatial clustering methods. The findings reveal that the collaborative service network in the study area is overall sparse, exhibiting a distinct “core–periphery” structure, with noticeable patterns of resource concentration and isolation. Residential areas can be categorized into three types based on their supply–demand characteristics: efficient-collaborative, transitional-mixed, and low-demand peripheral areas. The predominance of the transitional-mixed type indicates that most areas are currently in an unstable state of supply–demand adjustment. A key systemic mechanism identified in this study is the significant “collaborative reinforcement effect” between facility utilization rates and network centrality. Building on these insights, we propose a hierarchical optimization strategy consisting of “overall network optimization—local cluster coordination—individual facility enhancement.” This ultimately forms a comprehensive decision-support framework for “assessment—diagnosis—optimization,” providing scientific evidence and new solutions for the precise planning and efficient operation of urban charging infrastructure.

Overlapping communities are key characteristics of the structure and function analysis of complex networks. Shared or overlapping nodes within overlapping communities can either form subcommunities or act as intersections between larger communities. Nodes at the intersections that do not form subcommunities can be identified as overlapping nodes or as part of an internal structure of nested communities. To identify overlapping nodes, we apply a threshold rule based on the number of nodes in the nested structure. As the threshold value increases, the number of selected overlapping nodes decreases. This approach allows us to analyse the roles of nodes considered overlapping according to selection criteria, for example, to reduce the effect of noise. We illustrate our method by using three small and two larger real-world network structures. In larger networks, minor disturbances can produce a multitude of slightly different solutions, but the core communities remain robust, allowing other variations to be treated as noise. While this study employs our own method for community detection, other approaches can also be applied. Exploring the properties of shared nodes in overlapping communities of complex networks is a novel area of research with diverse applications in social network analysis, cybersecurity, and other fields in network science.