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The construction industry is an important material production sector of the national economy, and trade in goods and services between different industrial sectors in different regions may result in the transfer of embodied carbon emissions from the construction industry. A systematic identification of the relationships and structural characteristics of the embodied carbon transfer in the construction industry is crucial for rationally defining the responsibility for emission reduction and scientifically formulating emission reduction policies to promote the effective promotion of China's carbon emission reduction actions. Based on the calculation of input-output theory, this study constructs a multiregional input-output (MRIO) model of 31 provinces in China containing 28 industries to estimate the carbon emissions of the construction industry in 2017, it also combines the complex network theory to construct the industrial and regional embodied carbon transfer network of China's construction industry, and calculates the network structure indexes to deeply explore the spatial transfer network structure characteristics of the embodied carbon transfer between regions of China's construction industry in 2017. The results show that the construction, energy and building materials manufacturing sectors are at the core of the sectoral carbon transfer network structure, with strong network control. The embodied carbon transfer network between regions in the construction industry has a small-world character, more than 40% of all relevant regions have carbon transfer relationships with other regions, significant carbon emissions are transferred from the resource-rich, industrially well-endowed central-western and north-eastern provinces to the economically developed south-eastern coastal provinces. According to the results of the study, differentiated carbon emission reduction plans are formulated, and policy suggestions for optimizing the carbon emission reduction plan of the construction industry are put forward. [ABSTRACT FROM AUTHOR]

Introduction: With the advancement of society and technology, the importance of professional competency has been increasingly emphasized. This study aims to provide an in-depth analysis of the Baidu search index for "professional competency" from July 3, 2017, to October 6, 2023, across 34 provincial-level administrative regions in China. Methods: The study begins with a literature review to establish a theoretical framework. Subsequently, it delves into the fundamental concepts of complex networks, their parameters, and the principles of visible network graph algorithms. The application of system clustering and community detection algorithms is explored within this framework. Results: The empirical analysis starts with a provincial-level examination, involving the construction of visible network graphs, the exploration of scale-free networks, and discussions on regional system clustering. Following this, a nationwide analysis is conducted, focusing on the construction of visible network graphs to visualize the awareness of professional competency and studying community structures. Discussion: The findings provide insights into the awareness of professional competency across different regions. These insights are valuable for informing policy formulation and corporate decision-making, contributing to a deeper understanding of professional competency awareness and its implications. [ABSTRACT FROM AUTHOR]

With the growth of a city's economy, neighboring cities are gradually integrated to form an urban agglomeration. As travel activities driven by various travel demands frequently take place within an urban agglomeration, it is essential to understand the travel demand between cities and improve the intercity transportation system, which promotes the coordinated development of cities in an urban agglomeration. This paper presents our investigation of the travel demand characteristics of urban agglomeration cities. The Beijing-Tianjin-Hebei area and its passenger flows between transportation hubs in the different cities, which are part of a typical urban agglomeration in China and representative travel demand, are taken as the empirical study objects. First, we introduce a method to extract trip data using mobile phone call detail record (CDR) data, which carry rich geographical information on travelers and has been extensively used in recent transportation research. Based on trip data, directed weighted travel demand networks were constructed, with the nodes representing the transportation hubs and the edges representing passenger flows. The results showed that edge weights can be characterized by power-law distribution, which reveals the phenomenon that most passenger flows are concentrated among a few hubs, implying unbalanced travel demand in the urban agglomeration. Our empirical findings contribute to a method for applying large-scale location-based data to extract human mobility information and to understanding the nature of travel demand on the scale of an urban agglomeration. They also provide guidance to government agencies in developing appropriate transportation policies and enhancing infrastructure in the urban agglomeration. [ABSTRACT FROM AUTHOR]

As the core link of the air traffic control system, the evaluation and management of controllers' workload are of great significance for the rapid development of China's civil aviation industry and aviation safety. To address the problems of strong subjectivity and lag in the current controller evaluation methods, we propose to construct a flight conflict network and control network based on inter-aircraft flight conflict, controller control relationship and control transfer relationship by using the interdependent network theory. Based on the urgency of inter-aircraft conflict and the control difficulty of controllers, we set the side rights and construct an interdependent network model. Based on the constructed network model, the controller workload is evaluated by selecting the interdependent network index characteristics. Finally, the experimental analysis is carried out by program simulation and control data of Takasaki Airport. The results demonstrate that the method is able to evaluate the controller's workload. At the same time, it can quickly and accurately identify the key control nodes and provide assistance for controllers to allocate their efforts reasonably. Our proposed controller workload evaluation method enables quantitative analysis and predicts the future workload of controllers based on the potential conflict relationships between air posture and aircraft. The method has strong timeliness and objectivity. At the same time, the constructed interdependent network model can realize the identification of key aircraft in the control area and reflect the impact of key aircraft on the whole network, which can help controllers to perform better control work. [ABSTRACT FROM AUTHOR]

In this paper, we present the empirical investigation results on the urban public transport networks (PTNs) and propose a model to understand the results obtained. We investigate some urban public traffic networks in China, which are the urban public traffic networks of Beijing, Guangzhou, Wuhan and etc. The empirical results on the big cities show that the accumulative act-degree distributions of PTNs take neither power function forms, nor exponential function forms, but they are described by a shifted power function, and the accumulative act-degree distributions of PTNs in medium-sized or small cities follow the same law. In the end, we propose a model to show a possible evolutionary mechanism for the emergence of such network. The analytic results obtained from this model are in good agreement with the empirical results. [ABSTRACT FROM AUTHOR]

Intercity patient mobility reflects the geographic mismatch between healthcare resources and the population, and has rarely been studied with big data at large spatial scales. In this paper, we investigated the patterns of intercity patient mobility and factors influencing this behavior based on >4 million hospitalization records of patients with chronic kidney disease in China. To provide practical policy recommendations, a role identification framework informed by complex network theory was proposed considering the strength and distribution of connections of cities in mobility networks. Such a mobility network features multiscale community structure with "universal administrative constraints and a few boundary breaches". We discovered that cross-module visits which accounted for only 20 % of total visits, accounted for >50 % of the total travel distance. The explainable machine learning modeling results revealed that distance has a power-law-like effect on flow volume, and high-quality healthcare resources are an important driving factor. This paper provides not only a methodological reference for patient mobility studies but also valuable insights into public health policies. • More than 4 million multi-year mobility records from a national database were investigated. • The distribution of travel distance of patients differs from the results of general human mobility studies. • The network features multiscale community structure with "universal administrative constraints and a few boundary breaches". • An identification framework was proposed to understand cities' roles in the mobility network. • The explainable AI model revealed that distance and high-quality healthcare resources are two critical factors. [ABSTRACT FROM AUTHOR]

Abstract: Using the requisition papers of Chinese Nature Science Basic Research in management and information department, we construct the weighted network of research areas (WNRA). In WNRA, two research areas, which is represented by the subject codes, are considered to be connected if they have been filled in one or more requisition papers. The edge weight is defined as the number of requisition papers which have been filled in the same pairs of codes. The node strength is defined as the number of requisition papers which have been filled in this code, including the papers which have been filled in it alone. Here we study a variety of nonlocal statistics for WNRA, such as typical distance between research areas and measure of centrality such as betweenness. These statistical characteristics can illuminate the global development trend of Chinese scientific study. It is also helpful to adjust the code system to reflect the real status more accurately. Finally, we present a plausible model for the formation and structure of WNRA with the observed properties. [Copyright &y& Elsevier]

For a healthy industrial structure (IS) and stable economic development in China, this study proposes an improved link prediction algorithm (LP) based on complex networks. The algorithm calculates the similarity by constructing a mixed similarity index. A regional IS network model is built in the study, and the direction of IS adjustment is calculated with the mixed similarity indicators. In this study, the prediction accuracy of the proposed improved LP algorithm in the real network dataset is up to 0.944, which is significantly higher than that of the other algorithms. In the reality of IS optimization, industries of high similarity could be obtained through similarity algorithms, and reasonable coordinated development strategies are proposed. In addition, the simulated IS adjustment strategy in this study shows that it is highly sustainable in development, which is reflected in its lower carbon emissions. The optimization of IS adjustment could be achieved through IS network model and the improved LP algorithm. This study provides valuable suggestions for China's regional industrial structure adjustment. [ABSTRACT FROM AUTHOR]

Global competition for crude oil has increased in the past decade with the entry of industrializing nations such as China and India. Yet we still do not know much about the spatial structure of crude oil commodity trade and its evolution over time. In this paper, we apply complex network analysis to examine the geography of global crude oil flows and its evolution based on the United Nations commodity trade database from 1988 to 2013. Attention is given to the geo-visualization of the networks that trace the rise and decline of oil hubs. The results show that world crude oil is characterized by network characteristics that capture both small world and flat world properties. [ABSTRACT FROM AUTHOR]

The industry chain of industrialized construction is a key strategy for promoting the sustainable performance of China's construction industry. Its risk identification is the fundamental step to promote the development of the industry chain. The study was conducted in two phases. The first phase included an extensive literature review and case study analysis to document 32 key factors affecting the process of the industry chain of industrialized construction. In the second phase, 22 key factors influencing the development of the industry chain of industrialized construction in Shandong Province were screened through data collection and expert consultation. A complex network of industrialized construction risk associations (CNICRA) was developed to assess these risks by considering the interrelationship among risks, network nodes, and network edges, and the comprehensive degree indicators for improving the model's accuracy and resolution. The results show that enterprise collaboration level is the most important factor in the industry chain of industrialized construction. The industrialized system is the most transmittable factor of risk. This study investigated a list of risks in the industrialization of construction, optimized a complex network of risk association, and provided theoretical support for risk management of the industry chain of industrialized construction and understanding of risk response strategies for decision makers. [ABSTRACT FROM AUTHOR]

Rapid urbanization has led to deteriorated wetland water quality, reduced biodiversity, and fragmented wetland landscapes, which seriously threaten the sustainable development of regional ecology. Based on land use data of Dongying City, Shandong Province, in 2020, this study selected the landscape disturbance degree and landscape fragility index to construct a landscape ecological risk evaluation model and to analyze the spatial distribution characteristics of landscape ecological risk in Dongying City in 2020. The MSPA-Conefor-MCR model was used to extract the ecological network of wetlands in Dongying City, and the topological structure indices were quantitatively analyzed. Combined with the actual situation within the study area, the source sites to be optimized were identified by risk zoning and source importance; the ecological resistance surface was modified using landscape ecological risk, and the ecological network was optimized by simulating edge increase in order to evaluate the robustness of the ecological network before and after optimization and to verify the edge increase effect. The results show that the ecological risk in Dongying is high, mainly distributed in the central region and extending to the northeast, southeast, southwest, and northwest. A total of 131 ecological source sites (6 core and 125 resting-stone source sites) and 180 ecological corridors were extracted, and the whole ecological network was found to be less stable and to have stronger network heterogeneity using a topological analysis. By simulating 11 additional edges, the robustness of the optimized ecological network was significantly improved. Optimizing the simulated-edge increase can enhance the smoothness of ecological energy flow, which can provide a scientific basis for the construction of the ecological security pattern of wetlands in Dongying City. [ABSTRACT FROM AUTHOR]

This paper uses two physics-derived techniques, the minimum spanning tree and the hierarchical tree, to investigate the networks formed by CITIC (China International Trust and Investment Corporation) industry indices in three periods from 2006 to 2013. The study demonstrates that obvious industry clustering effects exist in the networks, and Durable Consumer Goods, Industrial Products, Information Technology, Frequently Consumption and Financial Industry are the core nodes in the networks. We also use the rolling window technique to investigate the dynamic evolution of the networks' stability, by calculating the mean correlations and mean distances, as well as the variance of correlations and the distances of these indices. China's stock market is still immature and subject to administrative interventions. Therefore, through this analysis, regulators can focus on monitoring the core nodes to ensure the overall stability of the entire market, while investors can enhance their portfolio allocations or investment decision-making. [ABSTRACT FROM AUTHOR]

Abstract: Shareholders are the owners of listed companies, and their relationships can directly affect the structure of the stock market. In this paper, we analyze the topological properties and evolution of the cross-shareholding networks of listed companies in the past 5 years in China from 2007 to 2011, an infrequently considered topic, by taking shareholders’ cross-shareholding relationships into account. This analysis arrives at a deeper insight into the inner characteristics of China’s stock market. We find that the cross-shareholding networks of listed companies with shareholders’ cross-shareholding relationships display statistical features that reveal the stock market’s complex relationships more precisely. In particular, the results show that when the shareholders’ cross-shareholding relationships are considered, first, the In-degree and Out-degree of the cross-shareholding networks follow power-law distribution and the of the linear regression analysis of the cumulative degree distribution is relatively higher; second, the modularity of the communities is larger; finally, both the number of members of top-ranked communities and the number of communities that have a large number of members are larger than those of which only considering the relationships between shareholders and listed companies are taken into account. Such cross-shareholding networks analysis taking shareholders’ cross-shareholding relations into account would be a helpful tool for supervisory departments and for stock market researchers to grasp the inner cross-shareholding relationships of listed companies in China, and it will be also helpful for the further researches about the “agent problems” in the stock markets from a whole point of view. [Copyright &y& Elsevier]

Abstract: Recently numerous studies have applied complex network based models to study the performance and vulnerability of infrastructure systems under various types of attacks and hazards. But how effective are these models to capture their real performance response is still a question worthy of research. Taking the Chinese railway system as an example, this paper selects three typical complex network based models, including purely topological model (PTM), purely shortest path model (PSPM), and weight (link length) based shortest path model (WBSPM), to analyze railway accessibility and flow-based vulnerability and compare their results with those from the real train flow model (RTFM). The results show that the WBSPM can produce the train routines with 83% stations and 77% railway links identical to the real routines and can approach the RTFM the best for railway vulnerability under both single and multiple component failures. The correlation coefficient for accessibility vulnerability from WBSPM and RTFM under single station failures is 0.96 while it is 0.92 for flow-based vulnerability; under multiple station failures, where each station has the same failure probability fp, the WBSPM can produce almost identical vulnerability results with those from the RTFM under almost all failures scenarios when fp is larger than 0.62 for accessibility vulnerability and 0.86 for flow-based vulnerability. [Copyright &y& Elsevier]

Abstract: In this paper, we studied the research areas of Chinese natural science basic research from a point view of complex network. Two research areas are considered to be connected if they appear in one fund proposal. The explicit network of such connections using data from 1999 to 2004 is constructed. The analysis of the real data shows that the degree distribution of the research areas network (RAN) may be better fitted by the exponential distribution. It displays small world effect in which randomly chosen pairs of research areas are typically separated by only a short path of intermediate research areas. The average distance of RAN decreases with time, while the average clustering coefficient increases with time, which indicates that the scientific study would like to be integrated together in terms of the studied areas. The relationship between the clustering coefficient and the degree k indicates that there is no hierarchical organization in RAN. [Copyright &y& Elsevier]

As is well known the novel coronavirus (COVID-19) is a zoonotic virus and our model is concerned with the effect of the zoonotic source of the coronavirus during the outbreak in China. We present a SEIS complex network epidemic model for the novel coronavirus. Our model is presented in fractional form and with varying population. The steady states and the basic reproductive number are calculated. We also present some numerical examples and the sensitivity analysis of the basic reproductive number for the parameters. [ABSTRACT FROM AUTHOR]

• Global plastic waste trade networks (GPWTNs) from 1988 to 2017 are established. • The spatiotemporal evolution of the GPWTNs is analyzed. • The direct and indirect impacts of China's import ban on the GPWTNs are evaluated. • Practical implications are given according to analysis of the GPWTNs. Millions of tonnes (teragrams) of plastic waste are traded around the world every year, which plays an important role in partially substituting virgin plastics as a source of raw materials in plastic product manufacturing. In this paper, global plastic waste trade networks (GPWTNs) from 1988 to 2017 are established using the UN-Comtrade database. The spatiotemporal evolution of the GPWTNs is analyzed. Attention is given to the country ranks, inter- and intra-continental trade flows, and geo-visual communities in the GPWTNs. We also evaluate the direct and indirect impacts of China's plastic waste import ban on the GPWTNs. The results show that the GPWTNs have small-world and scale-free properties and a core-periphery structure. The geography of the plastic waste trade is structured by Asia as the dominant importer and North America and Europe as the largest sources of plastic waste. China is the unrivaled colossus in the global plastic waste trade. After China's import ban, the plastic waste trade flows have been largely redirected to Southeast Asian countries. Compared with import countries, export countries are more important for the robustness of GPWTNs. Clearly, developed countries will not announce bans on plastic waste exports; these countries have strong motivation to continue to shift plastic waste to poorer countries. However, the import bans from developing countries will compel developed countries to build new disposal facilities and deal with their plastic waste domestically. [ABSTRACT FROM AUTHOR]

Natural disasters, such as typhoons and earthquakes, pose great challenges to power system resilience and power supply reliability. It is widely acknowledged that critical transmission lines play a significant role in enhancing the resilience and reliability of power systems under severe natural disasters. In order to improve the disaster resilience of power systems with renewables, a critical line identification approach is proposed in this work based on the complex network theory. Firstly, the concept of core skeleton network (CSN) of power systems with renewables is introduced and a two-step strategy is presented to optimise the CSNs of the concerned power system considering the variable outputs of renewables. Then, a statistical salience method incorporating edge salience and the state-of-the-art null model in complex network theory is proposed to identify critical lines of each CSN and the concerned power system with renewables. Finally, the effectiveness of the proposed critical lines identification approach is verified by simulations on the modified IEEE 118-bus system with renewables, Guangdong Provincial Power System (GPPS) and Zhejiang Provincial Power System (ZPPS) in China. Simulation results show that by using the proposed approach, the critical transmission lines can be effectively identified, not requiring a predefined number of critical lines. [ABSTRACT FROM AUTHOR]

Complex network response to node loss is a central question in different fields of science ranging from physics, sociology, biology to ecology. Previous studies considered binary networks where the weight of the links is not accounted for. However, in real-world networks the weights of connections can be widely different. Here, we analyzed the response of real-world road traffic complex network of Beijing, the most prosperous city in China. We produced nodes removal attack simulations using classic binary node features and we introduced weighted ranks for node importance. We measured the network functioning during nodes removal with three different parameters: the size of the largest connected cluster (LCC), the binary network efficiency (Bin EFF) and the weighted network efficiency (Weg EFF). We find that removing nodes according to weighted rank, i.e. considering the weight of the links as a number of taxi flows along the roads, produced in general the highest damage in the system. Our results show that: (i) in order to model Beijing road complex networks response to nodes (intersections) failure, it is necessary to consider the weight of the links; (ii) to discover the best attack strategy, it is important to use nodes rank accounting links weight. [ABSTRACT FROM AUTHOR]

Using homogenous partition of coarse graining process, the time series of Hang Seng Index (HSI) in Hong Kong stock market is transformed into discrete symbolic sequences S={S1S2S3⋯}, Si∊(R, r, d, D). Weighted networks of stock market are constructed by vertices that are 16 2-symbol strings (i.e. 16 patterns of HSI variations), and encode stock market relevant information about interconnections and interactions between fluctuation patterns of HSI in networks topology. By means of the measurements of betweenness centrality (BC) in networks, we have at least obtained 3 highest betweenness centrality uniform vertices in 2 order of magnitude of time subinterval scale, i.e. 18.7% vertices undertake 71.9% betweenness centrality of networks, showing statistical stability. These properties cannot be found in random networks; here vertices almost have identical betweenness centrality. By comparison to random networks, we conclude that Hong Kong stock market, rather than a random system, is statistically stable. [ABSTRACT FROM AUTHOR]