Complex Network Analysis Methods Research Articles

Transmission Mechanism of Influencing Factors in the Promotion and Application of Whole-Process Engineering Consulting

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.

Feature selection and interpretability analysis of compound faults in rolling bearings based on the causal feature weighted network

Feature selection is a crucial step in fault diagnosis. When rolling bearings are susceptible to compound faults, causal relationships are hidden within the signal features. Complex network analysis methods provide a tool for causal relationship modeling and feature importance assessment. Existing studies mainly focus on unweighted networks, overlooking the impact of the strength of causal relationships on feature selection. To address this issue, we propose a compound fault feature selection method based on the causal feature weighted network. First, we construct a weighted network using the incremental association Markov blanket discovery algorithm and Pearson correlation coefficient. Then, we quantify the importance of features by treating node strength as a centrality index and rank them to partition the feature subset. Finally, the optimal feature subset is obtained through a neural network with the accuracy of compound fault diagnosis as the threshold. Analysis of public datasets and comparative experiments demonstrate the advantages of our method. Compared to existing research, our method not only effectively reduces the number of optimal feature subsets to 11 but also improves the accuracy of compound fault diagnosis to 95.2%. Furthermore, we employ the SHapley Additive exPlanations to interpret the contribution of each feature in the optimal subset to the accuracy of compound fault diagnosis. This provides reference from both physical and network perspectives to feature selection and compound fault diagnosis in rolling bearings in practical working conditions.

Research on Potential Adverse Drug Reaction Forecasting Based on SAO Semantic Structure

With advances in medicine and biotechnology, the variety of available drugs has become more and more abundant. However, along with these innovations come complex adverse drug reactions (ADRs) as well. Extensive clinical trials are one of the best ways to reduce the incidence of drug reactions, but as the number of potential drug interactions grows, trials are becoming enormously time-consuming and costly. Hence, we set out to develop an alternative that could widely identify potential ADRs. Our solution is a “drug-ADR” network built from semantic subject-action-object structures, combined with complex network analysis and link prediction methods to reveal likely adverse reactions. Some similarity calculating methods also be used to improve our prediction accuracy. Evaluations of the results against the medical literature show that the predictions produced can be used as a weather vane for clinical trials, helping to save R&D time and capital costs. In addition, the framework can be used to provide useful guidance for discovering new drug indications or to inform the development of new drugs.

Analysis of the evolution characteristics of international ICT services trade based on complex network

Digital technology has brought profound changes in the services trade, particularly accelerating the growth of the international Information and Communications Technology (ICT) services trade. Understanding the evolving characteristics of the ICT services trade is pivotal for trade participants to formulate effective policies and enhance their competitive advantages. This study used the data from UN COMTRADE to construct ICT services trade networks spanning from 2004 to 2020. Using complex network analysis methods, it delved into several aspects of ICT services trade, including trade patterns, trade relationships, and participating countries. The findings revealed that central countries in ICT services trade are progressively dominating trade relationships. Moreover, many countries in the middle and lower tiers of the ICT services trade system tend to engage in bilateral trade with major economies or form trade alliances with a select few nations. Examining the evolution of trade participants, early adopter countries such as the United States, Ireland, and the United Kingdom have solidified stable competitive advantages in ICT services trade. Building upon these findings, the study puts forth several recommendations to enhance the competitiveness of the ICT services trade. This study not only enriches the existing knowledge base on ICT services trade networks but also provides valuable insights for shaping policies in this domain. The outcomes offer crucial guidance for interpreting cooperation and conflicts within the realm of ICT services trade.

Governance of the business environment based on food safety disputes: empirical analysis based on the case of “Cordyceps sinensis”

IntroductionThe use of the fungus Cordyceps sinensis (CS), which has a long history of use in China for its tonic properties, has sparked controversy based on the risk from high arsenic levels in CS.MethodsThis study collected all 24 online dispute cases related to CS from the China Legal Documents Network. Excel software was applied to fit the distribution of the sample data and analyze the trend of trial results of CS disputes and the tendency of professional claims trials. The analysis covers the trend of trial results of contract dispute cases involving CS products (CDCCS), and the tendency of the results of CDCCS, using correlation to analyze the factors influencing the results. We analyzed trial tendencies in CDCCS by the methods of complex network analysis (Gephi software) to explore each factor’s effect on the trial outcome of in these claims cases and to predict the trial outcomes of new cases. Simultaneously use KNN to train and predict CS cases.ResultsThe peak number of trial cases was in 2018, and there has been a gradual increase in the rate of losing claims on the claimant. Correlation analysis was used to obtain that the likelihood of the defendant winning a claim is positively correlated with DR, FSS, IDP, and KSP, and negatively correlated with the adoption of JIFDD (3), JIFDD (15), and APL. Gephi software was used to conduct a complex network analysis of the factors influencing the tendency of trial results in CS disputes, showing that noncompliance with FSS, existence of KSP, and submission of IDP evidence by the buyer are the direct factors influencing the seller’s defeat. The seller’s “failure to perform DR” is the key factor in the seller’s defeat in cases. On this basis a probability function is developed to predict a trial win rate of 0.7803 for freshman cases, classifier to predict the remaining four samples and new cases, and get the prediction accuracy is 0.8, 95% CI: (0.2836, 0.9949), and the latter was higher than the former.DiscussionIt is concluded that the attribute of CS products is a scientific issue, the jurisdiction of CS disputes is confusing and needs to be clarified, and the application of the provisions in judicial trials is cannot adapt to the current environment and needs to be resolved. This study advocates that the government design policy from the top and establish a study on the boundary between administrative and judicial functions to provide a theoretical guarantee to circumvent lazy administration and unscientific law enforcement. The government can provide a legal and regulatory system to improve food quality and regulate management.

Situation Assessment of Air Traffic Based on Complex Network Theory and Ensemble Learning

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.

Construction and Analysis of Multi-Species Ecological Network, a Case Study of the Southeast Qinghai–Tibetan Plateau

In recent years, rapid global changes have accelerated the loss of habitats and fragmentation of landscapes, emerging as primary drivers of the alarming decline in global biodiversity. Through the construction of ecological networks (ENs) that simulate the interactions between animal and plant species with their environment, it is possible to mitigate landscape fragmentation and the loss of biodiversity. In this study, we focused on the ecologically diverse southeastern region of the Qinghai–Tibetan Plateau (QTP) as our research area and developed a comprehensive Multi-Species Ecological Network (MEN) consisting of ten species. Through employing complex network analysis methods, we thoroughly examined the intra-species and inter-species interactions within the MEN, integrating the findings with the natural characteristics of the study area to yield valuable insights. The results of our study revealed considerable spatial variations in the MEN. Specifically, the western and eastern regions experienced significant ecological resistance, leading to fragmented ecological sources and a limited connectivity of ecological corridors. Furthermore, the application of complex network analysis revealed inadequate connectivity and stability in specific localized areas within the MEN. This emphasizes the pressing requirement for effective ecological preservation plans. Through this study, our aim is to advance research on multi-species ecological spatial networks and to offer novel perspectives and methodologies for biodiversity conservation and habitat maintenance in the Qinghai–Tibetan Plateau.

Analysis of muscle synergy and muscle functional network at different walking speeds based on surface electromyographic signal

An in-depth understanding of the mechanism of lower extremity muscle coordination during walking is the key to improving the efficacy of gait rehabilitation in patients with neuromuscular dysfunction. This paper investigates the effect of changes in walking speed on lower extremity muscle synergy patterns and muscle functional networks. Eight healthy subjects were recruited to perform walking tasks on a treadmill at three different speeds, and the surface electromyographic signals (sEMG) of eight muscles of the right lower limb were collected synchronously. The non-negative matrix factorization (NNMF) method was used to extract muscle synergy patterns, the mutual information (MI) method was used to construct the alpha frequency band (8-13 Hz), beta frequency band (14-30 Hz) and gamma frequency band (31-60 Hz) muscle functional network, and complex network analysis methods were introduced to quantify the differences between different networks. Muscle synergy analysis extracted 5 muscle synergy patterns, and changes in walking speed did not change the number of muscle synergy, but resulted in changes in muscle weights. Muscle network analysis found that at the same speed, high-frequency bands have lower global efficiency and clustering coefficients. As walking speed increased, the strength of connections between local muscles also increased. The results show that there are different muscle synergy patterns and muscle function networks in different walking speeds. This study provides a new perspective for exploring the mechanism of muscle coordination at different walking speeds, and is expected to provide theoretical support for the evaluation of gait function in patients with neuromuscular dysfunction.

How Does the Time-Varying Network Structure Evolve between the EU Carbon Futures Prices and Industrial and Energy-Related Indices? A Study Based on a Time-Varying T-Copula

Industrial and energy-related industries are major sources of carbon dioxide emissions, and their interdependence, as reflected in the financial field, has attracted the attention of scholars. For the purpose of exploring the evolutionary characteristics of the short-term dynamic correlation coefficient between the EU carbon futures price and the industrial and energy-related indices, this paper selected the settlement price of EU carbon emission quota futures, the MSCI energy I index on three dimensions, and the Dow Jones industrial index and West Texas crude oil futures price, as sample data. Using the time-varying t-copula model to measure the dynamic correlation coefficient between variables, the time-sliding window idea and coarse-grained method were combined to establish the correlation fluctuation mode, and a complex network theory and analysis methods were used to study the evolutionary traits of the time-varying network structure between the EU carbon price and the industrial and energy-related index. The results show that the transmission objects of the key correlation fluctuation modes in the network are stable and maintain their own state with a high probability. Second, the clustering effect exists in the transmission process. Some nodes with high mediating abilities are also the key correlation wave modes in the dynamic correlation evolution network. This study provides ideas for the study of the correlations between multiple variables and is also a useful reference for international investors.

Unravelling the landscape of global cobalt trade: Patterns, robustness, and supply chain security

As a strategic mineral resource, cobalt is widely used in electrical, mechanical, chemical, aerospace and other fields. It is of great significance to assess the spatiotemporal evolution and robustness of global cobalt trade to fully understand the global cobalt supply chain security in the new era. To this end, this paper constructs a global cobalt trade network dataset since 2001, employs various complex network analysis methods, portrays the spatiotemporal patterns and evolution process of the global cobalt trade networks, analyzes the topological structures of the global cobalt trade networks, and further measures their structural robustness to provide valuable references for the cognition of global cobalt trade security. The results show that: (1) Global cobalt trade volume has been growing, and the trade links have become increasingly close, gradually forming spatial patterns with obvious hierarchical structures and prominent hub countries. (2) The hierarchical structures of the global cobalt trade networks have undergone significant changes, and the dominant positions of China and the Democratic Republic of the Congo in the global cobalt trade networks have become increasingly prominent. (3) The backbone structures of the global cobalt trade networks have been expanding and differentiating, forming hub-and-spoke organizational patterns dominated by a few hub countries such as the USA, Germany and China. A few hub countries dominate the entire network backbone structure, but this dominance is not monotonously enhanced. (4) The overall robustness of the global cobalt trade networks shows a declining trend. The dominance of a few nodes and edges in the network structure is becoming increasingly prominent. The cobalt supply will face higher potential risks and challenges once an important node fails or an edge is interrupted. Finally, this paper proposes relevant policy recommendations to promote cobalt trade cooperation and build a secure and stable cobalt supply chain system.

Exploring the spatial characteristics of the human mobility network in rural settings of China's Greater Bay Area

Human mobility is an important carrier of material flow, information flow, capital flow, and technology flow between locations. A better understanding of human mobility in rural areas could inform efforts to achieve rural revitalization. However, there have been limited evaluations of the characteristics of human mobility in rural areas. Based on human movements estimated from mobile phone data, we constructed an inter-town human mobility network and applied a complex network analysis method to study the spatial characteristics of the human mobility network in rural areas, with China's Greater Bay Area as a case study. We found that rural towns with high centrality were mostly located at the convergence of developed megacities. Rural towns with most connections were not necessarily the most central in the inter-town human mobility networks, leading to anomalous values of the centrality. We demonstrated that the anomaly of centrality values emerged due to the community structure of the network. We identified nine communities in the network and found that the geographical borders of communities partly matched the urban administrative borders. However, the results also showed that many rural towns on the fringes of a city were dissolved in their adjacent developed cities, independent of the urban administrative boundary effect. The formation of communities cannot be explained only by the impacts of urban administrative boundaries, but also by the growth of large cities and the geographic dispersal of economic activities. Finally, we identified seven roles of rural towns based on their pattern of intra-community and inter-community connections. These findings provide important insights into the pattern of interaction among rural towns.

Identification of priority areas to provide insights for ecological protection planning: A case study in Hechi, China

The prolonged disregard for the Gross Ecosystem Product (GEP) has limited the effectiveness of regional ecological conservation. However, identifying and safeguarding areas with high GEP, and optimizing their distribution and spatial structure, are of great immense for preserving ecosystem services (ESs). Our goal is to refine the spatial configuration of ecological network (EN), and subsequently augment and sustain the stability of ESs. We have specifically chosen Hechi City, situated in the Karst region of southwest China, as our case study. Firstly, we devised a GEP evaluation system to pinpoint crucial areas as ecological sources. Subsequently, we utilized the circuit theory and minimum cumulative resistance model to construct the preliminary EN. Through the circuit theory model identified the key nodes in the EN and optimized the EN. Finally, by combining complex network analysis methods, we evaluated the robustness and topological characteristics of the EN. Our results showed that the area of ecological sources increased from 71 to 161 after optimization. The number of corridors increased from 161 to 423, resulting in an overall increment of 8.8% in the source area. The connectivity and stability of the optimized EN can be significantly enhanced. The addition of new nodes increases the overall importance of the network, making the importance of each node more balanced. Additionally, the complex network analysis method provided a detailed understanding about the spatial topology of the overall and local elements of the EN. The study results can provide a valuable reference for optimizing, restoring and evaluating ENs in karst areas.

Quantifying economic sectoral iron commodity use and related vulnerability in China's supply chains

With its widespread use in manufacturing and machinery, iron is a crucial metal for modern society. However, knowledge about the economic vulnerability of iron from multiple perspectives remains unexplored, especially for China, a country responsible for more than half of global iron and steel consumption. This study aims to quantify the inter-sectoral flows of iron commodities and the caused economic vulnerabilities, using an input-output model and complex network analysis methods. Results show that the residential building construction sector was the most vulnerable sector from a direct perspective. However, the final uses of certain services (e.g., the public administration and social organizations sector and business services sector) were more vulnerable concerning supply restriction from a consumption-based perspective. Based on the above findings, policy recommendations, such as circular economy and material substitutions are proposed to help improve resource efficiency and reduce the vulnerability of critical sectors from dependence on iron and steel.

Public emotional atmosphere during disasters: understanding emotions in short video comments on the Zhengzhou flood

Short videos are gaining popularity among Chinese netizens who use them to socialize and express themselves online, particularly during disasters. This study examines the characteristics of an emotional atmosphere through short video comments. We developed the Multimodal Social Short Video Crawler V1.0 to analyze 157,747 comments from 343 short videos about the “Zhengzhou flood.” First, we categorized comments into clusters and calculated cluster density using complex network analysis methods. Second, we employed computer-mediated content analysis to derive sentiment values in clusters. To capture the emotional atmosphere in short videos accurately, we developed a trust model to assign weights to both cluster density and sentiment value. Our findings reveal that the emotional atmosphere in short video comments indicates continuity and clustering traits. Furthermore, a correlation was found between cluster size and emotional valence, with the relationship trend varying between macro- and micro-clusters. Regarding theoretical contributions, this study enriches the theory of emotional atmosphere by proposing a mathematical model of emotional atmosphere that combines machine learning and complex network analysis, and introduces cluster density and emotional value calculation methods. Concurrently, this study highlights emotion’s importance in short video comments, which can enhance disaster response efforts in the future.

Verification of Geographic Laws Hidden in Textual Space and Analysis of Spatial Interaction Patterns of Information Flow

The rapid development of Internet technology has formed a huge virtual information space. In the information space, information flow has become a link of communication between objects. Information flow is an alternative or supplement to the traditional physical flow for the study of the spatial interaction of geographical entities. The research uses toponym co-occurrence and search index as information flow data, verifies the geographical laws hidden in the information space by spatial autocorrelation analysis and gravity model fitting, and analyzes the spatial interaction patterns of provinces in China in the information space by complex network analysis methods. The results show that: (1) information flow in the information space obeys Tobler’s first law of geography and Goodchild’s second law of geography. The spatial interaction represented by information flow has a distance decay effect. The best distance decay coefficients for toponym co-occurrence and the search index are 0.189 and 0.186, respectively. (2) The inter-provincial spatial interaction network of China shows a hierarchical pattern of the triangular primary network and diamond secondary network, and the ranking of provinces in the centrality analysis is basically stable, but the network hierarchy is deepening. The gravity center of spatial interaction is located in the east-central region of China. (3) The information flow-based interaction network is of higher asymmetry than the population mobility network, and its spatial structure is also obvious. This research provides a new idea for studying the spatial interaction of geographical entities in the physical world from the perspective of information flow.

Dynamic Characteristics and Evolution Analysis of China’s Rural Population Migration Networks from 2000 to 2020 Based on the Perspective of Regional Differences

The large-scale cross-regional migration of rural migrants is a significant feature of the current social system in China, affecting the sustainability of social and economic development. Using China National Census data and socio-economic panel data, we applied the complex network analysis method to construct a rural population migration network and analyze its characteristics, evolution, and mechanisms of network formation and changes. The research findings are as follows: (1) From 2000 to 2020, the rural population migration network developed rapidly, the network scope expanded continuously, regional participation gradually grew, and the inter-regional rural population migration relationship became increasingly complex. The overall network gradually evolved from an early segmented and loose structure to a monolithic and solid structure. (2) The in-degree centralization of the network remained higher than the out-degree centralization. The inbound migration pattern of the rural population was more compact than the outbound migration pattern, and the internal network displayed aggregated inbound population migration and dispersed outbound population migration flow characteristics. (3) The network had a distinctive “core–edge” character, with the core areas and the main migration flows in the network demonstrating high similarity across different periods. The overall network gradually developed around the core areas where the rural population moved in and out. (4) The influence mechanism between the later migration network and the geographical differences was more complex than in the early period. Differences in the regional development levels and geographical factors continued to influence the mobility choices of the rural population, whereas differences in income levels, cost of living, employment opportunities, and education funding only affected the rural migration network during specific periods. Based on these findings, reasonable migration policies should be formulated to ensure sustainable urban and rural development.

Research on price transmission in Chinese mining stock market: Based on industry

The strong impact of COVID-19 on the global mining market has caused severe fluctuations in the prices of mineral products and mining stocks. Meanwhile, geopolitical conflicts have exacerbated risks in minerals trade and mining stock transactions. In the face of uncertainties in the international economic landscape and volatility of stock prices, China, as the world's major mineral trading country, has become increasingly linked between its stock market and the mining economy. To clarify the characteristics of mining stock price fluctuations and the evolution of the transmission relationships, and identify the key nodes and main paths of price transmission, we select 100 Chinese mining stocks from January 2019 to October 2022, distinguish them according to the industry category, and use Granger causality test, minimum spanning tree model and complex network analysis method to study. The results show that: (1) Chinese mining stock prices have risen significantly since 2020, and there has been a “decoupling” phenomenon within the stock market, that is, the linkage between some mining stocks has weakened. (2) The stock price fluctuation characteristics and transmission effects of different mining industries are obviously different. Precious metal minerals (PM) have the most dramatic changes in price fluctuations, the most prominent hedging characteristics, and the rapid price response ability, which is the first to accept price transmission. rare earth and rare metal minerals (RE) are sensitive to price fluctuations and are usually the “leader” of the transmission path. Bulk non-ferrous minerals (BNFM) have the most stable price fluctuations and are closely related to other stocks, which is a “transit warehouse” in the transmission path. (3) The price transmission mechanism of Chinese mining stock market has gradually stabilized, and the main transmission paths of “Coal→Agricultural minerals (Agri)→BNFM→Steel” and “PM, Core minerals for new energy (NEM), and RE→BNFM” have been formed in 2022.

Transportation infrastructure network optimisation based on computer simulation using Sichuan Province, China as an example

PurposeThe purpose of this paper is to propose a method that can calculate the transportation infrastructure network service capacity enhancement given by planned transportation infrastructure projects construction. Managers can sequence projects more rationally to maximize the construction effectiveness of infrastructure investments.Design/methodology/approachThis paper designed a computational network simulation software to generate topological networks based on established rules. Based on the topological networks, the software simulated the movement path of users and calculated the average travel time. This software allows the adjustment of parameters to suit different research objectives. The average travel time is used as an evaluation index to determine the most appropriate construction sequence.FindingsIn this paper, the transportation infrastructure network of Sichuan Province in China was used to demonstrate this software. The average travel time of the existing transportation network in Sichuan Province was calculated as 211 min using this software. The high-speed railways from Leshan to Xichang and from Xichang to Yibin had the greatest influence on shortening the average travel time. This paper also measured the changes in the average travel time under two strategies: shortening the maximum and minimum priorities. All the transportation network optimisation plans for Sichuan Province will be somewhere between these two strategies.Originality/valueThe contribution of this research are three aspects: First, a complex network analysis method that can take into account the differences of node elements is proposed. Second, it provides an effective tool for decision makers to plan transportation infrastructure construction. Third, the construction sequence of transportation infrastructure development plan can effect the infrastructure investment effectiveness.

Structural Evolution of Global Soybean Trade Network and the Implications to China.

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.

Analysis on the Spatial Structure and Interaction of Aviation Network and Tourism Efficiency Network in Major Cities in China

Tourism efficiency is crucial for measuring sustainable tourism development. Examining the relationship between aviation and tourism efficiency networks is key to promoting their synergistic development in China's urban areas. This study employs various methods, such as complex network analysis method, entropy-weighted TOPSIS, tourism efficiency gravity model, and quadratic assignment procedure, to analyze the networks' spatial structure evolution characteristics and interaction effects. Results show that (1) China's major cities' aviation network has improved its organizational efficiency and formed a "double rhombus-single axis" spatial evolution pattern of the axis-spoke network. The number of intermediary networks and hub cities in the central and western regions has increased. (2) The tourism efficiency network adopts a "honeycomb" structure pattern with the simultaneous layout of "point-to-point" and "star" networks. The network's tourism efficiency follows "Pareto's Law," and tourism cities above the second level form a club group development. The tourism efficiency development potential area is shifting to the southwest. (3) The aviation and tourism efficiency networks exhibit a clear trend of synergistic evolution with a "path locking" phenomenon between them. Differences in tourism resource endowment, labor advantage, and capital advantage positively impact the aviation network's structure. Conversely, differences in revenue capacity and market scale negatively impact the structure. The aviation scale advantage, openness, intimacy, and influence exhibit decreasing positive effects on the tourism efficiency network's structure.