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Investigating the significant "roles" within financial complex networks and their stability is of great importance for preventing financial risks. On one hand, this paper initially constructs a complex network model of the stock market based on mutual information theory and threshold methods, combined with the closing price returns of stocks. It then analyzes the basic topological characteristics of this network and examines its stability under random and targeted attacks by varying the threshold values. On the other hand, using systemic risk entropy as a metric to quantify the stability of the stock market, this paper validates the impact of the COVID-19 pandemic as a widespread, unexpected event on network stability. The research results indicate that this complex network exhibits small-world characteristics but cannot be strictly classified as a scale-free network. In this network, key roles are played by the industrial sector, media and information services, pharmaceuticals and healthcare, transportation, and utilities. Upon reducing the threshold, the network's resilience to random attacks is correspondingly strengthened. Dynamically, from 2000 to 2022, systemic risk in significant industrial share markets significantly increased. From a static perspective, the period around 2019, affected by the COVID-19 pandemic, experienced the most drastic fluctuations. Compared to the year 2000, systemic risk entropy in 2022 increased nearly sixtyfold, further indicating an increasing instability within this complex network. [ABSTRACT FROM AUTHOR]

Under the path of sustainable development, the key to achieving green and low-carbon transformation lies in green technology innovation (GTI), and how to effectively coordinate the relationship between green finance (GF) and GTI is an issue worth studying. This paper constructed an evaluation system of GF and GTI and combined them with the coupled coordination degree model to explore their coordination of Chinese provinces from 2012 to 2019. Then, the core network evolution and spatial structure characteristics of GTI and GF were studied using the modified gravity model. Finally, based on the link prediction, the general future network prediction is made to provide guidance and direction for the future GTI and GF development and construction. The results found that the coordination level between GF and GTI has been continuously improved from 0.356 to 0.436. The core network structure is keeping changing with their connection becoming more complex, and there is still room for optimization. Network centrality characteristics show that the spatial spillover effects are stronger in the more economically developed regions. The overall network possibility prediction shows the potential network connections in different urban agglomerations. This paper provides a certain reference role for China and developing countries to predict the GF and GTI cooperation network development in the future. [ABSTRACT FROM AUTHOR]

As a project construction organization model, whole-process engineering consulting (WPEC) is currently being promoted in China's engineering consulting industry. However, various factors create numerous obstacles that hinder this model's effective promotion and application. One essential task is to deeply analyze the transmission mechanism of the influencing factors in the promotion and application of WPEC, and explore effective pathways for promoting and applying this model. This paper systematically organizes and summarizes 10 primary influencing factors and 38 secondary influencing factors for promoting WPEC. Using the complex network analysis method, a network of influencing factors for promoting WPEC, a network structure, and motif analyses are conducted. The results show that the influencing factors have a complex transmission mechanism, and complex network analysis can reveal the laws of their construction and their causes. This paper provides a systematic basis and theoretical support for the effective formulation of policies related to the promotion and application of WPEC. [ABSTRACT FROM AUTHOR]

As a special branch of global trade, the trade of agricultural products has an important impact on food security and the environment. In this paper, we studied international trade network of agricultural products from 2000 to 2016 as a whole and in part. We explored the overall characteristics of the network, analyzed the evolution of communities and identified core countries of the communities. The results show that the structure of the trade network became increasingly complex and the trade relations became closer over time. There were four major communities in the network, whose primary core countries were Germany, the United States, Brazil, and China. Since 2007, the community represented by China has disappeared, and the community pattern of the network has been in a three-pillar state and basically stable. We discuss the actual roles of certain trading countries, the formation of communities and the impact of economic events on agricultural products trade. This paper reveals the underlying patterns of the agricultural products trade and provides a way to track its evolution over time. [ABSTRACT FROM AUTHOR]

Evaluating the importance of nodes in coal transportation networks and identifying influential nodes is a crucial study in the field of network science, vital for ensuring the stable operation of such complex networks. However, most existing studies focus on the performance analysis of single-medium networks, lacking research on combined transportation, which is not applicable to China's coal transportation model. To address this issue, this paper first establishes a static topological structure of China's coal-iron-water combined transportation network based on complex network theory, constructing a node importance evaluation index system through four centrality indicators. Subsequently, an enhanced TOPSIS method (GRE-TOPSIS) is proposed based on the Grey Relational Entropy Weight (GRE) to identify key nodes in the complex network from local and positional information dimensions. Compared to previous studies, this research emphasizes composite networks, breaking through the limitations of single-medium network research, and combines gray relational analysis with entropy weighting, enhancing the objectivity of the TOPSIS method. In the simulation section of this paper, we establish the model of China's coal-iron-water combined transportation network and use the algorithm to comprehensively rank and identify key nodes in 84 nodes, verifying its performance. Network efficiency and three other parameters are used as measures of network performance. Through simulated deliberate and random attacks on the network, the changing trends in network performance are analyzed. The results show that in random attacks, the performance drops to around 50% after damaging nearly 40 ordinary nodes. In contrast, targeting close to 16 key nodes in deliberate attacks achieves a similar effect. Once key nodes are well protected, the network exhibits a certain resistance to damage. [ABSTRACT FROM AUTHOR]

Purpose: Clarifying the risk evolution mechanism of housing construction for work-safety management is essential. Existing studies have inadequately discussed the risk-accumulation process in housing construction. Therefore, this study aimed to use the complex network theory and risk allocation mechanisms to explore the evolution of risk factors. Design/methodology/approach: The authors analysed a database of housing construction accidents in China from 2015 to 2020 to identify risk factors. Moreover, the causal relationship between risk factors was determined through a systematic analysis of the logical sequence of risk factors. A complex network was used to construct a risk network for housing construction accidents (RNHCA). Findings: The risk matrix method was used to define the factor risk threshold, and a risk value was assigned based on the correlation between risk factors. This contributes to the examination of the evolution mechanism of risk networks in the process of risk factor transmission. The case verification results show that the RNHCA quantitative assessment model can better evaluate the system risk status of housing construction accidents. Furthermore, this model can identify the key risk factors and risk chains with high risk in the evolution of the risk network. Research limitations/implications: Accident investigation reports need to be classified and processed to analyse the evolution law of risk networks under different scales of construction project, such as high-rise buildings, middle-rise buildings, and low-rise buildings. Practical implications: This study clarified the risk evolution process of complex systems in housing construction and provided a new method for analysing accidents. Originality/value: This study clarifies the risk value allocation of risk factors in the transmission process and reveals the process of risk factor evolution in housing construction. This study explains the individual risk factors that form a systemic risk through the transmission chain. Moreover, this paper clarified the transformation relationship between system risk and accidents. The paper also provided a new perspective for risk analysis. [ABSTRACT FROM AUTHOR]

The existing literature on the volatility forecasting less considered the co‐movement among stock markets from the spatial dimension. This paper builds the hybrid convolutional neural networks (CNNs)–gated recurrent unit (GRU) model for volatility forecasting under high frequency financial data based on transaction information and the topological characteristics constructed through the complex network of multi‐market symbol patterns. The hybrid neural network CNN‐GRU combines the advantages of CNN automatically extracting features for the input indicators and GRU processing long and short‐term serially dependent features, which can better improve the forecasting accuracy. The empirical results show that with the integration of topological characteristics as the indicators based on complex network, the deep learning model has a significant improvement of one‐step and multi‐step volatility forecasting accuracy for the China's and the US stock markets. The research in this paper provides a complete index system from the spatial dimension and a more accurate and robust volatility forecasting method under the high‐frequency financial data. [ABSTRACT FROM AUTHOR]

Taking the Sino–US trade war as the background, we study the co-movement changes on horizontal return and volatility of the daily logarithmic return data of global stock indices from 2016 to January 23, 2020 from static and dynamic perspectives. Through static tools such as correlation coefficient matrices, complex networks, and Granger causality, it is found that the trade war has significantly strengthened the correlation between the Chinese stock market and the global stock market, and highlighted the position of the Hong Kong stock market. On dynamic analysis, the news impact surface shows the asymmetry of the co-movement for positive and negative news. The aDCC-EGARCH model is used to observe the dynamic co-movement changes between the Chinese stock market and the global stock market. It shows that trade conflicts have enhanced the co-movement between the Chinese stock market and the global stock market to varying degrees. At the same time, the Chinese stock market is strategically tilted towards the Asian market. Based on the research results in this paper, countries around the world need to pay more attention to the risk contagion between stock markets after the outbreak of the trade war. China's inland markets should be alert to risk contagion from the Hong Kong market. [ABSTRACT FROM AUTHOR]

• Global recycling trade is analyzed by network method and top1 network perspective. • The evolution of global recycling trade networks shows three distinct stages. • The top1 recycling import network obviously differs from export network. • Recycling policy of economies has a serious impact on recycling trade pattern. Recycling trade transfers recyclable resources and relevant environmental pressures on a global scale to solve resources limitation problem for some economies and alleviate urgent environment protection pressure for other countries/regions. Correspondingly, recycling trade relationships among economies are changing profoundly with the constant adjustment of economic and environmental policies. Based on complex network approach and top network analysis, this paper aims to comprehensively explore the structural features and their evolutionary patterns of global recycling trade networks from the macro, medium and micro levels by using global multi-region input-output data. The results are: At the macro level, the depth of global recycling import and export markets has strengthened continuously. The breadth of recycling exports showed a "pre-concentration, post-diversification" trend, and the import market breath demonstrated an opposite trend. At the medium level, the community structure of the global recycling export market increased rapidly in the early stage and became stable in the medium and late stages, while that of import market rapidly declined in the late stage, which means the import market was more sensitive to relevant policies and economic events. At the individual level, developed economics have been trying to transfer recycling products to developing economies, causing the former dominate global recycling export markets and the latter dominate the import markets. Recycling policies of economies, especially the developing economies have a serious impact on the global recycling trade pattern. Therefore, China's new announcement in 2018 for prohibiting import "foreign waste" will inevitably have a huge impact on the global recycling market. [ABSTRACT FROM AUTHOR]

With the rapid development of the air transportation industry, the air traffic situation is becoming more and more complicated. Determining the situation of air traffic is of great significance to ensure the safety and smoothness of air traffic. The strong subjectivity of assessment criteria, inaccurate assessment results and weak systemic assessment method are the main problems in air traffic situation assessment research. The aim of our research is to present an objective and accurate situation assessment method for air traffic systems. The paper presents a model to assess air traffic situation based on the complex network theory and ensemble learning. The air traffic weighted network model was introduced to systematically describe the real state of an air traffic system. Assessment criteria based on the complex network analysis method can systematically reflect the operational state of an air traffic weighted network system. We transformed the air traffic situation assessment into a binary classification, which makes situation assessment objective and accurate. Ensemble learning was introduced to improve the classification accuracy, which further improves the accuracy of the situation assessment model. The model was trained and tested on the dataset of the East China air traffic weighted network in 2019. Its average classification accuracy is 0.98. The recall and precision rates both exceed 0.95. Experiments have confirmed that the situation assessment model can accurately output air traffic situation value and situation level. Furthermore, the assessment results are consistent with the real operational situation of the air traffic in East China. [ABSTRACT FROM AUTHOR]

Improving the carbon sequestration capacity (CSC) of an ecosystem by optimizing urban ecological networks is one of the effective ways to achieve the goal of "carbon neutrality" in the world. The contradiction between the irreplaceable ecological function and economic development of Yancheng City is prominent. Therefore, taking Yancheng City as an example, this paper adopted the morphological spatial pattern analysis–minimum cumulative resistance (MSPA-MCR) model to establish the ecological network of Yancheng City in 2020 and combined it with complex network theory to evaluate its ecological base, network quality, and CSC. The results show that the ecological network of Yancheng City has obvious characteristics of coastal cities. There is a significant positive correlation between CSC and the clustering efficiency of ecological sources, and improving the clustering efficiency of vegetation and water ecological nodes is conducive to enhancing the CSC of ecological networks. In terms of functional restoration of ecological networks, four types of 13 ecological stepping stones and 12 ecological corridors have been designed to strengthen the connectivity and balance of the network, and the improvement of network robustness before and after optimization verifies that the optimization scheme is reasonable and effective. This study improved the optimization method of ecological networks in Yancheng City based on enhancing the CSC of ecological nodes, which provided a theoretical framework and practical reference for the realization of global strategic goals of carbon neutrality. [ABSTRACT FROM AUTHOR]

China experiences a serious shortage of soybean supplies and relies heavily on international trade with high vulnerability and large uncertainty, which maybe sows food security risks. It is of great significance to analyze the structural evolution of the global soybean trade network and its implications to China for ensuring food security. This paper constructed a global soybean trade network (GSTN) and analyzed the structural evolutionary characteristics of GSTN from 2000 to 2020 using the complex network analysis method and simulated the impact of targeted destruction on China through scenario analysis. The results showed that GSTN was gradually complex exhibiting a small word and a scale-free network property. The global soybean exporter was dominated by some major soybean-producing countries in America. The US played an important role in maintaining GSTN's robustness. China was the world's largest soybean importer; unfortunately, its soybean imports relied heavily on a few countries, and the anti-interference ability of China's soybean trade tended to decline. Therefore, China's soybean trade was increasingly vulnerable to being tightly controlled by other countries when some uncertain factors occurred, such as trade frictions and changes in policy decisions from importing and exporting countries. The US and Brazil were key countries with significant soybean trade ties to China. To assess the impact of the two countries on China's soybean trade, targeted destruction method was used through destroying them in the network. Targeted destruction scenario analysis indicated the two countries played important roles in the anti-interference ability of China's soybean trade. Brazil played a positive role in China's control of soybean trade flows, while the US did not. Some policies for China's soybean production and international trade were proposed. A balance between the domestic production and import of soybean is needed. Optimizing the soybean trade import system and seeking more trade partners is crucial. Improving soybean self-sufficiency is the fundamental way to reduce the high-import dependence. The study provided some insights for coping with international market fluctuations and improving the sustainability of China's soybean trade. [ABSTRACT FROM AUTHOR]

Forest products trade has a critical role to play in both global economic development and environmental protection. Based on the trade data of global forest products, raw forest products, and processed forest products from 2000 to 2020, this paper constructed a trade network and analyzed its pattern by using indicators such as density, average distance, out-strength, and in-strength. The results show that from 2000 to 2020, the global trade network of forest products has been increasingly connected. Compared with the trade of raw material forest products, the trade network of processed forest products is more closely connected and its transport efficiency is higher. Countries with high GDP such as USA, China, and Germany are leading trading countries in forest products. Countries with abundant forest resources are the major exporters of raw forest products, while labor- and capital-abundant countries are major exporters of processed forest products. Countries with abundant labor and capital and high GDP are the main importers of raw forest products. [ABSTRACT FROM AUTHOR]

Delving into the spatiotemporal evolution of the railway network in different periods can provide guidance and reference for the planning and layout of the railway network. However, most of the existing studies tended to model the railway data separately and compare the network indices of adjacent periods based on the railway data of different periods, thus failing to integrate the railway network in different periods into a unified framework for evolution analysis. Therefore, this paper used the railway data from 2008, 2010, 2015, and 2019, and analyzed the spatiotemporal integration of the railway network evolution based on the complex network theory and the self-organizing maps (SOM) method. Firstly, this study constructed the geographical railway network in the four years and probed into how the network feature indices changed. Then, it used the SOM method to capture the spatiotemporal integration of the railway network evolution in multi-time series. Finally, it clustered the change trajectory of each city node and unveiled the relationship between the evolution of city nodes and the hierarchy of urban systems. The results show that from 2008 to 2019, the railway network feature indices showed an upward trend and that the expansion pattern of the railway network could be divided into the core–peripheral pattern, belt expansion pattern, strings of beads pattern, and multi-center network pattern. The evolution of the change trajectory of the city nodes was highly related to the hierarchical structure of the urban system. This study helps to understand the evolution process of the railway network in China, and provides decision-making reference for improving and optimizing China's railway network. [ABSTRACT FROM AUTHOR]

The lead-lag relationship between stocks is an interesting phenomenon, which has been empirically seen to widely exist in stock markets. This paper aims to discover the dynamic patterns of the daily lead-lag relationships between stock pairs, to detect the features of the discovered dynamic patterns, and to explore which factors significantly affect the emergence of the feature. To this end, a series of statistical analyses is conducted to find that the (longest) successive lead-lag days satisfy a power-law distribution in the two mainland stock markets in China, which answers the question regarding the dynamic pattern. Note that the heavy tail of the power-law distribution is the core of the discovered dynamic pattern. A formal and solid definition of the lead-lag effect is provided by statistical testing, and then the corresponding detection method is designed and applied to obtain the heavy tail. Finally, an empirical study of the detected stocks with lead-lag effect is further conducted via an exponential random graph model (ERGM). Our work adds new knowledge to the lead-lag phenomenon in the financial domain, provides a formal definition of the lead-lag effect and proposes a new detection method benefiting future studies on the lead-lag relationship in financial markets. It further contributes to the existing relevant literature by a deep understanding of which factors cause the emergence of the power-law distribution discovered. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of air pollution management and promote the efficiency of coordinated inter-regional prevention and control, this study analyzes the interaction of O3 in Qilihe District, Lanzhou City, China. Data used for analysis was obtained from 63 air quality monitoring stations between November 2017 and October 2018. This paper uses complex network theory to describe the network structure characteristics of O3 pollution spatial correlation. On this basis, the node importance method is used to mine the sub-network with the highest spatial correlation in the O3 network, and use transfer entropy theory to analyse the interaction of pollutants between regions. The results show that the O3 area of Qilihe District, Lanzhou City can be divided into three parts: the urban street community type areas in urban areas, the township and village type areas in mountain areas and the scattered areas represented by isolated nodes. An analysis of the mutual influence of O3 between each area revealed that the impact of O3 on each monitoring station in adjacent areas will vary considerably. Therefore these areas cannot be governed as a whole, and the traditional extensive management measures based on administrative divisions cannot be used to replace all other regional governance measures. There is the need to develop a joint prevention and control mechanism tailored to local conditions in order to improve the accuracy and efficiency of O3 governance. [ABSTRACT FROM AUTHOR]

Based on the complex network of industry space, this paper investigates the industry structure optimization in Jiangxi Province in China. For the prefecture-level cities in Jiangxi Province, the location quotient is applied to obtain the specialized sub-industries. The conditional probability is used to analyze the interdependencies between industries. The complex network of industry space is presented to illustrate the relatedness among industries and analyze the space and structure of the regional overall industry. The index of transitional potential is proposed to measure the potential of a non-specialized industry which will become a specialized one in the future. The transformation difficulty index of industry is obtained to analyze the difficulty level of the transformation of the original specialized industry set to a new specialized one of a city. The results show that more industries have positive than negative interdependencies with one another in Jiangxi Province; rare industries specialize in cities at higher-level GDP, and common industries dominate the cities at lower-level GDP; it is more difficult for rarer non-specialized industries to become specialized ones, more common non-specialized industries can become specialized ones more easily, and the cities with higher GDP can transform industries more easily. By discussing the industries that have positive interdependencies with existing specialized industries, the detailed direction of industry structure optimization is analyzed. Finally, the policy implications are proposed, and these can be applied to other developing countries. • The conditional probability is used to analyze the interdependencies between industries. • The complex network of industry space is presented to illustrate the relatedness among industries. • The index of transitional potential is used to measure the potential of a non-specialized industry becoming specialized. • The transformation difficulty index is the difficulty level of the transformation of the originally specialized industry. • The industry structure can be optimized by developing the industries that have positive interdependencies with existing specialized industries. [ABSTRACT FROM AUTHOR]

The original intention of virtual water trade (VWT) is to help water-scarce areas adjust the crop trade structure to alleviate the water shortage problem. However, China's existing virtual water trade (VWT) must effectively alleviate the problem. This paper's structural features and stability of the VWT network (VWTN) in China's major crop trade between 2000 and 2017 were characterized using complex network theory. The results in terms of time scale showed that the total content of VW in China's major crops increased by ~23.6% for 18 years. Trade relations among most regions remained stable, whereas a few areas changed: Jilin, Henan, and Heilongjiang played essential roles in the output network, and so did Guangdong, Shanghai, and Fujian in the input network. Attributed to policy adjustment, Henan and Jilin became more prominent in the output network with the gradual decline of Jiangsu's position. Regarding spatial scale, the cumulative distribution of degrees consistent with the power–law relationship showed high variability and vulnerability of China's VWTN, especially when nodes were weighted. Since areas with more VWT partners/content provided connectivity to those with fewer partners/content, it is worthwhile to focus on developing protection policies for critical areas. The virtual water trade from North China (water-poor) to South China (water-rich) is contrary to the distribution of water resources. A similar situation showed in a global world that the long-term supply relationship would aggravate the water resources shortage and food security. Furthermore, we suggest combining network theory and VWT to lay the foundation for the invulnerability research of VWTN and the optimal regulation of crops. [ABSTRACT FROM AUTHOR]

• A practical and feasible protection strategy for EC maintaining in highly urbanized areas was proposed. • Key patches for EC maintaining were identified by using stepping stone and complex network theories. • The protection rate for key patches by the new policies were assessed. Maintaining and restoring ecological connectivity (EC) are important measures of mitigating habitat isolation caused by urbanization. Considering the prominent contradiction of land use in highly urbanized areas, the EC protection measures proposed in the previous studies, such as expanding the ecological sources and widening the ecological corridors, are unrealistic. By using the stepping-stone and complex network theories, this paper simulated the ecological network (EN), evaluated the centrality of each ecological source and stepping-stone, and analysed the network robustness under different damage scenarios, then identified the optimal strategy and key patches for EC maintaining. Meanwhile, this paper evaluated whether the key patches are protected by China's new policies, which are, Ecological Protection Red Line (EPRL) and the Three Lines One Permit (TLOP). The results were as follows: (1) The EN is composed of 53 ecological sources and 291 stepping-stones, with 591 links between them. (2) The network stability can be optimally maintained by prioritizing the protection of stepping-stones with high centrality. (3) The 53 ecological sources and 51 stepping-stones with the highest centrality can ensure the stable operation of the EN, that is, they are key patches for EC maintaining. (4) Compared with EPRL, the protection rate for ecological sources and stepping-stones are significantly improved by TLOP; however, stepping-stones with high centrality require stronger protection. This study enriched the research cases of complex network theory applied in landscape ecology, and provided references for the improvement of policies in the future. [ABSTRACT FROM AUTHOR]

Various construction accidents are proven to be caused by multiple unsafe behaviors (e.g., wrong use of PPE), but the risk transmission among different behaviors remains unclear. This paper provides insight into risk transmission through behavioral risk chain that leads to accidents from a system safety perspective. To better understand the coupling mechanism of various unsafe behaviors, integrate different behavioral risk chains and present the risk transmission process, a directed-weighted complex network (DWCN) method was adopted. Historical urban railway construction accidents in China are investigated to extract behavioral risk chain. A DW-BRCNA is applied to integrated behavioral risk chain and the behavioral risk transmission characteristics are explored and clarified by the five network properties, including degree and degree distribution, node strength and node strength distribution, average path length and diameter, weighted clustering coefficient and betweenness centrality. The results show that DW-BRCNA has the characteristics of a small-world, scale-free and hierarchical network, indicating that some unsafe behaviors are of greater importance in the process of risk transmission through behavioral risk chains. In addition, risk transmission in critical behavioral risk chains is more potentially to lead to accidents. This study proposed a new perspective of accident causation analysis from risk transmission among unsafe behaviors. It explains the risk transmission characteristics by a DWCN method based on behavioral risk chains. The findings also provide a practical guidance for developing control strategies on sites to prevent risk transmission and reduce accidents. [ABSTRACT FROM AUTHOR]

In this paper, the complex network of the urban functions in Shenzhen of China under the lockdown of the corona virus disease 2019 (COVID-19) is studied. The location quotient is used to obtain the dominant urban functions of the districts in Shenzhen before and under the lockdown of COVID-19. By using the conditional probability, the interdependencies between the urban functions are proposed to obtain the complex networks of urban functions and their clusters. The relationships between the urban functions, and the overall and cluster characteristics of the urban functions before and under the lockdown of COVID-19 are analyzed based on the complex networks. The mean degree and mean weighted degree of the primary categories of the urban functions are obtained to discuss the classification characteristics of the urban functions before and under the lockdown of COVID-19. Then, the differences and changes of the urban functions before and under the lockdown of COVID-19 are compared, and the corresponding policy implications under the lockdown of COVID-19 are presented. The results show that under the lockdown of COVID-19, the correlation of the urban functions is stronger than that before the lockdown; the common urban functions are more useful and essential, and finance, fine food and medical treatment are important; public service and government departments have the most positive relationship with other urban functions, and finance service has the highest spatial agglomeration distribution trend; and the cluster characteristics of urban functions are more related to people's livelihood, and the urban functions show incomplete and cannot be operated for long term. [ABSTRACT FROM AUTHOR]

In the double carbon background, riding the wind of new energy vehicles and the battery high nickelization, nickel resources rise along with the trend. In recent years, due to the influence of geopolitical conflicts and emergencies, as well as the speculation and control of international capital with its advantages and rules, the world may face price and security supply risks to a certain extent. Therefore, to obtain the most objective trade redistribution strategy, this paper first constructs the nickel material trade network, identifies the core trading countries and the main trade relations of nickel material trade, and finds that the flow of nickel material mainly occurred between a few countries. On this basis, a trade redistribution model is constructed based on the maximum entropy principle. Taking Indonesia, the largest exporter, and the largest trade relationship (Indonesia exports to China) as examples, the nickel material redistribution between countries when different supply risks occur are simulated. The results can provide an important reference for national resource recovery after the risk of the nickel trade. [ABSTRACT FROM AUTHOR]

Accompanied with the increasing complicated global value chain (GVC) networks is the carbon emission transfers among countries. Utilizing the complex network analysis alongside quadratic assignment procedure (QAP), this paper detects the community structure and influencing forces of the emission transfers under GVCs. The results imply that the bipolar structure of the network transformed gradually to tripolar owing largely to the surging of carbon emissions from China. Evidence on the existence of environmental Kuznets curve (EKC) in the emission transfers from high-income countries to low-income countries, and a U-shape relationship transfers in the reverse direction, suggesting that growing carbon emissions from both low- and high-income countries transferred to other high-income countries gradually. Gaps in technology, especially in patent applications, between source and destination countries played an important role therein. [ABSTRACT FROM AUTHOR]

In this paper, according to the characteristics of floods, the closeness centrality of complex network is improved to analyze the evolution of flood disaster social stability risks. Based on the investigation conducted on the flood occurred in Hunan Province in August 2013, and related papers, the topology map for the evolution of flood social stability risk is analyzed and constructed by means of the improved closeness centrality. On this basis, the evolution of social stability risks incurred in floods is divided into three grades, in which such risks as house collapse, crops ruining, looting shopping, social panic etc. produce the maximum impacts on the entire evolution system. So, during the disaster relief, priority should be given to resolve the difficulties in meeting the basic necessities that are closely associated with people's life. [ABSTRACT FROM AUTHOR]

With the rapid development of China's economy and society, China's railway transportation system has been dramatically improved in terms of its scale and operational efficiency. To uncover the underlying relationship between urbanization and railway network structure, this paper examines the evolution of China's railway transportation system from 1999 to 2019 by applying complex network theory. The results show that China's railway network (CRN) has become more connected, more "small-world" and more heterogeneous since the beginning of the twenty-first century. Based on the train flow and train travel distance, the evolutionary course of CRN is found to undergo two apparent stages, with a turning point in 2007. By calculating the regional railway connection index (RRCI), it is revealed that the planned core cities in different regions act as bridges connecting the regions to the rest of the whole network. [ABSTRACT FROM AUTHOR]

In this paper, we selected Xilingol League in Inner Mongolia, China, as the research area, based on the land-use data of five Landsat remote sensing images taken between 1980 and 2015. Then, we calculated the complex network eigenvalues, such as the average shortest path, betweenness centrality, and degree, to identify the key land-use types, stability, and ecological environment change regularity from the perspective of the land-use system. Finally, we used the Canonical Correspondence Analysis (CCA) method to explore the main driving forces behind changes in the land-use system, to provide scientific support for the study of changing trends in land-use, and regional grassland ecological management. The findings are shown below. First, in the last 35 years, grasslands have always been the major type of change in land-use transfer matrices. Grasslands play a controlling role in the whole land-use system. Second, grassland and cultivated land are the major "transfer out" type of land in the Xilingol area, while construction land and water area belong to the major "transfer in" type of land. Third, the average shortest path values of four transition matrix networks were all less than 1.5, indicating that the land-use system becomes less stable, but the average shortest path values of these four networks present an increasing trend, leading to a more stable development of the land-use system. However, on the whole, it shows an upward trend, and the land-use system is moving in a stable direction. Fourth, the average annual rainfall, population, topographic factors, GDP, and distance from settlements play a prominent role in determining the spatial distribution of change in land-use in Xilingol. [ABSTRACT FROM AUTHOR]

[Display omitted] • Complex network reveals the spatiotemporal propagations of extreme precipitation events (EPEs) in China. • EPEs over 218 river basins are classified into six communities with different seasonality from May to October. • There exist propagations of EPEs along the Yellow, Haihe, Liaohe and Songhua River Basins within one to eight days. Extreme precipitation events (EPEs) cause catastrophic losses to human lives and social economy. Focusing on the wet season from May to October, this paper formulates the complex network to characterize the spatiotemporal propagations of EPEs over 218 river basins in China. Specifically, based on the daily Multi-Source Weighted-Ensemble Precipitation version 2, the event synchronization along with significance test facilitates the complex network of EPEs from 1979 to 2020. By characterizing the topology of EPEs, the results show that synchronous EPEs generally occur over the Hetao Area of Yellow River Basin, Haihe River Basin and Northeast China. The complex network of EPEs classifies the 218 river basins into six communities. In May and June, more than 50 % of EPEs generally occur in South China. From July to September, over 60 % of EPEs tend to be observed in the Huang-Huai-Hai Plain, upper-middle reaches of Yellow River Basin, Northeast China and Southwest China. In October, about 65 % of EPEs happen in South and Southwest China. In the meantime, less than 10 % of EPEs occur in Northwest China. Among the synchronous EPEs starting in late June or early July and peaking in late July to early August, there exist propagations along the Yellow River, Haihe, Liaohe and Songhua River Basin within one to eight days. Eastward moisture transport along this pathway within four to six days drives the propagation of synchronous EPEs. The complex network analysis of this paper provides useful information for early warning and forecasting of EPEs and flood hazards. [ABSTRACT FROM AUTHOR]

The continuous increase of carbon emissions is a serious challenge all over the world, and many countries are striving to solve this problem. Since 2020, a widespread lockdown in the country to prevent the spread of COVID-19 escalated, severely restricting the movement of people and unnecessary economic activities, which unexpectedly reduced carbon emissions. This paper aims to analyze the carbon emissions data of 30 provinces in the 2020 and provide references for reducing emissions with epidemic lockdown measures. Based on the method of time series visualization, we transform the time series data into complex networks to find out the hidden information in these data. We found that the lockdown would bring about a short-term decrease in carbon emissions, and most provinces have a short time point of impact, which is closely related to the level of economic development and industrial structure. The current results provide some insights into the evolution of carbon emissions under COVID-19 blockade measures and valuable insights into energy conservation and response to the energy crisis in the post-epidemic era. [ABSTRACT FROM AUTHOR]

In order to analyze topological properties and vulnerable elements of urban road complex network, 98 lines and 128 nodes are selected by taking Shiyan city in China as an example. Firstly, the definitions of the urban road network's running state vulnerability and structural vulnerability are proposed in this paper, and the assessment indexes are given respectively. From the perspective of the urban road network topology's structural characteristics and the traffic demand characteristics, the internal relationship of the running state vulnerability and the structural vulnerability is introduced in order to show the necessity and reasonableness of the urban road network vulnerability assessment. Secondly, the concepts of the urban road network element's structural-running state parameters are proposed to identify the source of network vulnerability. The Original Method is used to acquire the topology of the urban road network. Finally, with static vulnerability assessment principle, the structural vulnerability index is calculated on the premise that the cascading failure is not considered. With dynamic vulnerability assessment principle, the running state vulnerability index is calculated on the premise that the propagation effects of traffic congestion are considered. The results show that the vulnerability of road network is influenced by two factors: network topology structure and network running state. [ABSTRACT FROM AUTHOR]

This paper analysed the evolution of landslide research and research foci in different countries. The data comprise 3105 landslide SCI articles published between January 1977 and June 2015 from the Web of Science. The data are extracted under interaction constraints of the journal title, category, and keywords. The complex network method is used for the analysis. First, from the perspective of topics and methods, the evolution is systematically assessed by generating a co-citation network of the articles and a semantic cluster analysis. Second, from the perspective of topics and landslide-related disasters, the focus in different countries is discussed by generating co-occurrence networks. These networks are the co-occurrence of the countries and keywords, and the co-occurrence of countries and landslide-related disaster phrases. The main conclusions are as follows: (1) landslide susceptibility analysis and methods of machine learning are popular research topics and methods, respectively. The topics change through time, and the article output is influenced by increasing landslide-related disasters, increasing economic losses and casualties, a desire for a more complete and accurate landslide inventory, and the use of effective methods, such as geographical information Science (GIS) and machine learning. (2) The research focus in each country is related with the country-specific disasters or economic costs caused by landslides to some degree. In addition to Italy and the USA, China is the country most commonly affected by landslides, and it should develop its own landslide database and complete in-depth studies of disaster mitigation. [ABSTRACT FROM AUTHOR]

A well-functioning public bicycle system relates not only to its mode of operation, vehicle allocation, rental station layout and vehicle leasing configuration, but also the bicycle network structure and its formation. However, the latter aspects have been widely overlooked in China. Here, we help to further attract more researchers to conduct relevant studies and make suggestions for the development of public bicycle transport in many small and medium-sized cities across the world. We demonstrate how to explore the public bicycle network structure of a county-level Chinese city - Yixing - known for its clay ware and tourism. We show that complex network theory and shortest path analysis technology are useful in characterizing the public bicycle network structure, in aspects such as network topology, the spatial distribution of sub-networks and traffic flows. Finally, the paper proposes relevant urban planning strategies. [ABSTRACT FROM AUTHOR]

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A clear regional imbalance exists in the e-sports industry in terms of the industry's contribution to the overall development of the economy. Available research suggests that this imbalance is a result of regional supporting policies, social culture integration and differences among countries. Therefore, to rationalize the overall structural planning for e-sports industrial association network (IAN), this paper probes into the structure of e-sports IAN based on the complex network theory. Firstly, the key factors affecting the development of the e-sports industry are identified, and an evaluation index system (EIS) is established. Secondly, e-sports IAN is analyzed from two angles, namely, the entire network and enterprise node, based on complex network theory. This study uses an experimental design for gathering data from the respondents. Finally, the clustering structure of e-sports IAN is studied based on modularity, and the network is divided into clusters. The results verify the effectiveness of complex network theory in the structural analysis of e-sports IAN. The economic ties between regional e-sports enterprises enhance slightly in the six years. Compared with the density of the national e-sports industrial network, the region has a certain advantage in e-sports industrial development in 2014. The findings of the present study fill the gaps mentioned in the paper and significantly benefit practitioners and stakeholders as well as help in identifying the salient industries and key areas in e-sports IAN. [ABSTRACT FROM AUTHOR]

• The strongest carbon intensity sectors were the greatest contributors to the increase in intersectoral CO2 emissions. • The strongest export intensity sectors were the largest contributors to the decrease of intersectoral CO2 emissions. • The strongest interaction linkages were linkages based in construction. • Key interaction patterns are identified. To identify the interaction pattern features and driving forces of intersectoral CO 2 emissions, this paper studies the interaction pattern types, interaction linkage strengths and key interaction patterns of 28-sector CO 2 emissions flow networks in China from 1997 to 2015 via network motif analysis. Network motif analysis is a useful tool for measuring interactions between agents and their function in networks. This paper extends network motif analysis by investigating the weighted edges of motifs to observe the function of motifs. The results show that in 1997–2002 and 2008–2015, the strongest carbon intensity sectors, including electricity production, metal smelting and gas production, were the greatest contributors to the increase in intersectoral CO 2 emissions. From 2003 to 2007, the strongest energy intensity sectors, including electricity production and fuel processing, were the greatest contributors. In contrast, the strongest export intensity sectors, including other electronic equipment, leather manufacturing, instrumentation, and textile, were the largest contributors to the decrease in intersectoral CO 2 emissions. Second, the strongest interaction linkages were between construction and nonmetallic products and metal smelting, machinery and construction and metal smelting, construction and nonmetallic products, construction and metal smelting. Third, the key interaction patterns for the increase in intersectoral CO 2 emissions were patterns such as infrastructure-export, manufacturing intensive, energy consumption, and energy tracing, while the export-intensive pattern and high export pattern were the key interaction patterns for the decrease in intersectoral CO 2 emissions. This paper is beneficial to the establishment of a coordinated emissions reduction mechanism across sectors. [ABSTRACT FROM AUTHOR]

Forest and grass ecological space is the key component of the ecosystem and plays a vital role in regulating the carbon, water, and energy cycle. The long-term exploitation of forest and grass ecological space and huge population pressure have gradually degraded the function of China's ecosystem. Therefore, forest and grass ecological space plays an important role in maintaining the stability of the ecosystem. The relationship between forest and grass ecospatial network structure and ecosystem service has been the focus of research. In this study, the forest and grass ecospatial network is constructed based on the minimum cumulative resistance (MCR) model. Then, the topological indicators (degree, weight clustering coefficient, node weight, unit weight, weight distribution difference, betweenness, PageRank) of the forest and grass ecospatial network were calculated by combining the complex network theory to analyze the relationship between these topological indicators and the three ecosystems (water retention, soil conservation, carbon storage). Based on the ecological significance of topological indicators, we identified ecologically fragile areas and proposed areas and directions for optimizing the ecospatial structure. Results show that the spatial distribution of the three ecosystem services in the southeast region of China is higher than that in the northwest region of China and shows a gradual decrease from the east to the west. The degree, node weight, unit weight, PageRank, and betweenness were highly significant and positively correlated with the three ecosystem services, among which PageRank had the highest correlation with water retention (p < 0.01, R2 = 0.835). Based on the spatial distribution characteristics of the different topological indicators, the quantitative relationship between the structural characteristics of the forest and grass ecospatial network and ecosystem services is clarified, revealing the intrinsic connection between ecological processes and ecosystem services. Through rational optimization of the forest and grass ecospatial network, ecosystem services can be effectively improved and ecosystem stability can be enhanced. [ABSTRACT FROM AUTHOR]

The lengthening of the global value chain (GVC) and the deepening of the transnational labor division system have led to frequent cross-border flows of metal values worldwide, and the pattern of the international metal trade is facing reconstruction. This paper combines a new trade accounting framework and a complex network model to construct global metal domestic value added (DVA) and foreign value added (FVA) flow networks based on global multi-regional input‒output (MRIO) tables for 2000 and 2007–2022 and then analyzes their structural topological features and spatio-temporal evolution trends at the macro, medium, and micro levels. Then, the main driving factors are investigated using the exponential random graph model (ERGM). The results show that the DVA and FVA networks are expanding and have obvious aggregation effects and small-world characteristics, that the community divisions show a trend toward regional integration and worldwide multipolarity, and that Germany is the most important transportation hub and trade medium in the FVA network, while China and the United States are, respectively, the largest creator and consumer of the world's metal value flow. In addition, the DVA network is more susceptible to node attribute effects such as GDP, trade openness, population size and foreign direct investment, while the FVA network is more susceptible to exogenous network effects such as geographic distance and colonial ties. [ABSTRACT FROM AUTHOR]

Clarifying the relationship between new energy and economic growth is an essential prerequisite for achieving the goal of carbon peaking and carbon neutrality. This paper utilizes a combination of nonlinear analysis methods, including transfer entropy, Bai–Perron (BP) structure point breakpoint detection, and complex network theory, to examine the coupling relationship between China's new energy and economy across different stages and sectors. An analysis of the network's topological properties shows that a complex coupling driving relationship exists between the new energy industry and the other 11 economic sectors. In particular, the agricultural sector exerts a significant driving force on the new energy industry, with the most extended duration of influence. Besides, the IT and construction industries also play a driving role in the new energy industry, and the latter's impact is earlier than that of the IT industry. The empirical results can guide the government and makers to formulate long-term sustainable action plans for different economic sectors. • Transfer entropy, BP structure break point detection and complex network are used. • 33 net transfer entropy symbol mode complex networks are constructed. • The coupling relationship between China's new energy and economy is complicated. • The agriculture industry has a large driving force on new energy industry. [ABSTRACT FROM AUTHOR]

Previous studies on the water–energy nexus mainly focused on the calculation and comparison of resource consumption at the national or regional level, lacking interprovincial sector-sector transfer analysis. In this study, the intensity of water and energy consumption of various sectors in China was calculated, the key nodes and paths of resource networks were identified, and countermeasures for resource conservation were proposed from the new perspective of the "dual saving" and "bidirectional saving" of water and energy. The results showed that the metallurgical industry (Me) in Jiangsu and the chemical industry (Ch) in Hebei and Jiangsu had high node strength in the water and energy network and were key sectors in China with "dual saving" effects of water and energy. The construction industry, Ch, Me in Jiangsu, electricity and hot water production and supply industry in Beijing, and Me in Hebei had high node strength in the water-related energy network and energy-related water network, significantly supporting the "bidirectional saving" effect of water and energy in China. The electrical equipment industry (El) in Jiangsu → El in Zhejiang, El in Zhejiang → El in Shanghai frequently appeared in key paths, which could effectively reduce the resource consumption of the entire network. [ABSTRACT FROM AUTHOR]

Civil aviation freight transport (CAFT) in China plays an important role in social and economic development. However, research on the morphological structure of China's CAFT network and its influence factors remains rare. In this paper, we focus on this topic by using a new data set of China's domestic routes from 2015 to 2019. With the complex network theory, we make an empirical study on the morphological structure of China's domestic CAFT network, and try to explore the nexus between the attributes of the network and the influence factors. The results show that China's domestic CAFT network is in line with the small world network model, which has a low average path length and a high clustering coefficient. The centrality of the network presents a saddle distribution with dense in the east and west, while sparse in the central. The results also indicate that there are many localized networks dominated by a few core airports. A number of regional hub cities exist, which are mainly located in the west of China. The development of China's CAFT highly depends on the civil aviation passenger transport (CAPT) network, and is highly affected by GDP, the industrial structure and the geographical location of cities. Finally, we give several policy suggestions, including developing the freight dedicated airports, establishing independent freight transport network system, vigorously developing core and intermediate hub cities, as well as constructing the regional network. • We study China's domestic civil aviation freight transport network by using a new dataset. • We analyze the network attributes and their influence factors at the city level. • A number of regional hub cities exist, mainly located in the west of China. • Civil aviation passenger transport, GDP and geographical location are the critical influencing factors of the network. [ABSTRACT FROM AUTHOR]

Due to globally uneven distribution of forest resources and timely difference of supply and demand, forest products are difficult to achieve market equilibrium among neighboring countries. The global forest product trade not only alleviates the contradiction of supply and demand, but also ensures the sustainable development of the economic construction for each country. Previous literature analyzed the structure and evolution mechanism of forest product trade from different perspectives, but ignored the competitive relationships among forest product importers. Based on complex network theory, this paper selected global wood forest product trade data from 2004 to 2016. Then we constructed global unweighted and weighted wood-forest-product competitive network from import perspective, and three indicators (density, average path length, and clustering coefficient), core–periphery model and two indicators of competitive intensity were introduced to study the spatial competitive evolution of global wood forest product trade among countries. The results showed that there was wide competition in global wood forest product trade among continents, and the tightness of competitive relationships gradually decreased with time. Secondly, core countries and periphery countries were clearly identified in the competitive network, and China, Japan, the U.S., were the main core countries influencing the evolution of core–periphery structure. Finally, the U.S. played an important role in the global competition system. But with the increment of demand for Asian market, China and other Asian countries gradually became the center of wood-forest-product competition. The research tried to provide some references for importers to follow the laws of global wood forest product trade, and formulate the corresponding policy under constructing sustainable society and facing climate change. • The paper considered the competitive relationship of global wood forest product trade. • TThe paper constructed global unweighted and weighted wood-forest-product competitive network, and studied spatial competitive evolution of global wood forest product trade. • The U.S. played an important role in the global competition system, but China and other Asian countries gradually became the center of global wood-forest-product competition. [ABSTRACT FROM AUTHOR]

Second emission trading market is not active in China so far, and the reason mainly lies in that enterprises do not have strong initiative for transaction. Thus it's meaningful to study related guiding mechanisms from the perspective of enterprises behavior. In this paper, an evolutionary game model and a small-world network model considering stock heterogeneity are constructed based on four given guiding mechanisms, and a simulation study is conducted through China Zhejiang emission trading market. The result shows that: (1) the optimal guiding mechanism is dynamic and changes from reward-punishment for both enterprises with surplus stock and enterprises with insufficient stock to reward-punishment for enterprises with insufficient stock when trading preferential profit coefficient increases to 1. (2) when penalty coefficient changes, the optimal guiding mechanism is the reward-punishment for both enterprises with surplus stock and enterprises with insufficient stock and keeps unchanged. (3) the enterprises with insufficient stock are more sensitive to penalty coefficient and trading preferential profit coefficient, especially to the latter, so it's better to stimulate the emission rights purchasing side and formulate positive reinforcement methods. The study of this paper can provide decision-making suggestions for Chinese government to stimulate the activity of second emission trading market. [Display omitted] • Four guiding mechanisms are given based on implementation objects. • An evolutionary game model is constructed. • A complex network model is constructed considering stock heterogeneity. • The optimal guiding mechanism is simulated and dynamically analyzed. • The policy recommendations are highlighted. [ABSTRACT FROM AUTHOR]

As the passenger railway network is expanding and improving, the internal connections and interdependence in the network are rising. Once a sudden geological hazard occurs and damages the network structure, the train service is prone to large-scale halt or delay. A geographical railway network is modeled to analyze the spatial distribution characteristics of the railway network as well as its vulnerability under typical geological hazards, such as earthquakes, collapses, landslides and debris flows. First, this paper modeled the geographical railway network in China based on the complex network method and analyzed the spatial distribution characteristics of the railway network. Then, the data of geological hazards along the railway that occurred over the years were crawled through the Internet to construct the hazard database to analyze the time–space distribution characteristics. Finally, based on the data of geological hazards along the railway and results of the susceptibility to geological hazards, the vulnerability of the geographical railway network was evaluated. Among these geological hazards, the greatest impact on railway safety operation came from earthquakes (48%), followed by landslides (28%), debris flows (17%) and collapses (7%). About 30% of the lines of the geographical railway network were exposed in the susceptibility areas. The most vulnerable railway lines included Sichuan–Guizhou Railway, Chengdu–Kunming Railway and Chengdu–Guiyang high-speed Railway in Southwest China, Lanzhou–Urumqi Railway and Southern Xinjiang Railway in Northwest China, and Beijing–Harbin Railway and Harbin–Manzhouli Railway in Northeast China. Therefore, professional railway rescue materials should be arranged at key stations in the above sections, with a view to improving the capability to respond to sudden geological hazards. [ABSTRACT FROM AUTHOR]

As an essential sub-network of the global liner shipping network, China's international liner shipping network was the earliest to be affected by the COVID-19 and also had a significant impact on the global shipping network. This paper uses Automatic Identification System (AIS) data to analyze the impact of COVID-19 on the typical route networks and major ports of China's international liner shipping. On this basis, the changes in network efficiency and connectivity under the failure of important nodes is simulated. The research finds that, during the epidemic period, the scale of China's international liner shipping network increased, with more routes gathering at fewer hub ports. Still, the overall connectivity and connection strength declined. Meanwhile, the epidemic caused fluctuations in container volume and the mismatch of ship cargo capacity supply, in which China-U.S. routes was the most prominent. From the view of node, the competitiveness of China's mainland ports was significantly promoted during the epidemic. In addition, ports such as Busan, Singapore, and Hong Kong substantially impacted China's international liner shipping network. The current study might be helpful for the industry management departments and related companies to prepare contingency plans, thus enhancing the resilience of the logistics chain and ensuring the stability of the global supply chain. [ABSTRACT FROM AUTHOR]

Research on the price fluctuation transmission of the carbon trading pilot market is of great significance for the establishment of China’s unified carbon market and its development in the future. In this paper, the carbon market transaction prices of Beijing, Shanghai, Tianjin, Shenzhen, and Guangdong were selected from December 29, 2013 to March 26, 2016, as sample data. Based on the view of the complex network theory, we construct a price fluctuation transmission network model of five pilot carbon markets in China, with the purposes of analyzing the topological features of this network, including point intensity, weighted clustering coefficient, betweenness centrality, and community structure, and elucidating the characteristics and transmission mechanism of price fluctuation in China’s five pilot cities. The results of point intensity and weighted clustering coefficient show that the carbon prices in the five markets remained unchanged and transmitted smoothly in general, and price fragmentation is serious; however, at some point, the price fluctuates with mass phenomena. The result of betweenness centrality reflects that a small number of price fluctuations can control the whole market carbon price transmission and price fluctuation evolves in an alternate manner. The study provides direction for the scientific management of the carbon price. Policy makers should take a positive role in promoting market activity, preventing the risks that may arise from mass trade and scientifically forecasting the volatility of trading prices, which will provide experience for the establishment of a unified carbon market in China. [ABSTRACT FROM AUTHOR]

Complex network theory is a widely used tool in the empirical research of financial markets. Two-mode and multi-mode networks are new trends and represent new directions in that they can more accurately simulate relationships between entities. In this paper, we use data for Chinese listed companies holding non-listed financial companies over a ten-year period to construct two networks: a two-mode primitive network in which listed companies and non-listed financial companies are considered actors and events, respectively, and a one-mode network that is constructed based on the decreasing-mode method in which listed companies are considered nodes. We analyze the evolution of the listed company co-holding network from several perspectives, including that of the whole network, of information control ability, of implicit relationships, of community division and of small-world characteristics. The results of the analysis indicate that (1) China’s developing stock market affects the share-holding condition of listed companies holding non-listed financial companies; (2) the information control ability of co-holding networks is focused on a few listed companies and the implicit relationship of investment preference between listed companies is determined by the co-holding behavior; (3) the community division of the co-holding network is increasingly obvious, as determined by the investment preferences among listed companies; and (4) the small-world characteristics of the co-holding network are increasingly obvious, resulting in reduced communication costs. In this paper, we conduct an evolution analysis and develop an understanding of the factors that influence the listed companies co-holding network. This study will help illuminate research on evolution analysis. [ABSTRACT FROM AUTHOR]

After a major debt crisis occurs in a listed company, the stock prices of related enterprises may also fluctuate sharply, resulting in the spread of debt risk to more enterprises. Taking the stocks of listed companies as network nodes, we constructed a complex stock market network over three periods of time through the logarithmic return rate of stocks for the three periods of prophase, metaphase, and anaphase of the debt crisis. We studied the topological characteristics of the network and destructiveness over the three periods. Finally, the minimum spanning tree was used to construct a network and the community structure of the network. The empirical analysis took the debt crisis of the China Evergrande Company as an example to analyze the impact of its major debt crisis on the Chinese stock market. The research findings were as follows: First, the debt crisis increased the inter-industry connections within the network, that is, the correlations between enterprises in different industries was enhanced. Second, the closeness of the cross-industry connections increased the connectivity efficiency of the network, but compared with the other two periods, the debt crisis in the metaphase was less stable. Third, community research showed that in the metaphase of the debt crisis, the enterprises became the core nodes of the network. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, the development of the blue economy is considered from the perspectives of integrity, relevance, and operability. First, to further expand blue living space and achieve blue growth, development thinking must shift from supporting the development of a single agent to supporting the coordinated development of multiple agents. Thus, we set a list of 116 blue products. Second, we construct a conceptual model of the national blue economic system space. Taking China as an example, the product data, firm data and province data in China's blue economic system are collected and processed based on the needs of the study, and the basic dataset for constructing China's blue economic system space is obtained. Third, based on the blue product space, the blue firm network and the blue province network of China, as well as the mapping relationship that existed among them in 2010, China's blue economic system space in 2010 is constructed. Finally, evolutionary maps of China's blue economic system space from 1985 to 2018 are drawn to identify the spatial structure and characteristics of China's blue economic development. We use application examples to verify that the national blue economic system space can play an important role in assisting countries in conducting systematic multicriteria assessments of blue growth strategies, and the use of such a complex network makes countries more likely to formulate evidence-based policies without being influenced by vested sectoral interests. [ABSTRACT FROM AUTHOR]

The purpose of this study is to analyze the topological structure dynamics of the complex network of stocks before and after the outbreak of the COVID-19, so as to provide a basis for preventing financial risks. We calculate Pearson correlation coefficient between enterprises according to logarithmic rate of return and trading volume ratio of enterprises' stocks, and then constructed a complex network of stock market price and volume before and after the outbreak of the COVID-19. First, through thresholding and heat map imaging of the correlation matrix, the change characteristics of the correlation between various industries in 2019 and 2020 are studied. Second, the node degree, average weighted degree, graph density, clustering coefficient, and average clustering coefficient are used to study the topological structure change of the complex network of stock correlation. Third, the principle of node betweenness centrality is used to analyze the characteristics of a complex network after removing the core nodes. The research shows that, first, under the influence of the COVID-19 pandemic, the correlation among industries has the characteristics of industrial clusters, that is, the correlation in a industry is strengthened. In addition to banking, the correlation between industries has weakened, and the correlation between the banking industry and other industries has strengthened. Second, the node difference in betweenness centrality of core nodes in 2020 is higher than that in 2019, indicating that the network stability in 2019 is higher than that in 2020. These two points indicate that under the influence of the COVID-19 epidemic, the complex network topology of China's entire stock market has changed, and companies need to undertake countermeasures in the face of the crisis to effectively prevent and control systemic risks. [ABSTRACT FROM AUTHOR]