Complex Network Perspective Research Articles

Research on coal mine safety risk evolution and key hidden dangers under the perspective of complex network

In order to find out the main causes of coal mine safety accidents and improve the pertinence of coal mine safety risk management and control, the identification and analysis of coal mine safety risks and hidden dangers are carried out based on the analysis of coal mine accident reports. Combing the complex network theory, a complex network model for the evolution of coal mine safety risks is constructed. The key elements that affect coal mine safety risk accidents are obtained through quantitative research on the characteristic indicators of the complex network model of coal mine safety risks. And the key nodes of coal mine safety risk spread network are obtained through network interference to the overall efficiency. The research results show that the complex network of coal mine safety risks illustrate the characteristics of a small-world network, and the spread of a certain risk is likely to cause coal mine safety accidents. Strengthening the risk management and control of hidden dangers with higher intermediate centrality can isolate the spread of coal mine safety risks and reduce the possibility of coal mine accidents.

The Impact of Mental Activities and Age on Brain Network: An Analysis From Complex Network Perspective

The Impact of Mental Activities and Age on Brain Network: An Analysis From Complex Network Perspective

Water and Energy Conservation across Provinces and Sectors in China: Based on a Complex Network Perspective

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.

Cryptocurrency: A new player or a new crisis in financial markets? —— Evolutionary analysis of association and risk spillover based on network science

In recent years, cryptocurrency has attracted widespread research attention. Against the backdrop of financial globalization, the high volatility of cryptocurrency prices may pose a threat to the stability of the entire financial system. To explore the potential association and transmission of financial risks between cryptocurrency and traditional financial assets, this paper constructs a complex network using the Granger causality method and the DY spillover method. Based on data from 24 categories of assets or index returns from different markets, networks of association and risk spillover between cryptocurrency market and traditional financial markets are established under different time periods, the study covers the period from January 2015 to November 2023. Analyzing the mutual influence between cryptocurrency and traditional financial markets from the perspective of complex networks, and evaluating the market positioning, risk characteristics, and investment potential of cryptocurrency. Research findings indicate that cryptocurrency possesses not only monetary characteristics but also functions as an investment tool, significantly impacting the stability of traditional financial markets. Furthermore, different types of cryptocurrencies vary in market functions, risk characteristics, and investment potentials. Future research could further explore the nonlinear and dynamic associations between cryptocurrency and traditional financial markets, as well as the micro mechanisms and behavioral factors within cryptocurrency markets.

Institutional Investor Information Networks and Stock Price Synchronicity

ABSTRACT The inefficiency of information flow in the capital market has led to a significant increase in stock price synchronicity, which has emerged as a critical challenge to the development of emerging capital markets. Based on a complex network perspective, this article investigates the relationship between institutional investor information networks and stock price synchronicity using data from Chinese public fund heavy holdings between 2011 and 2022. Empirical tests show that network correlation significantly reduces stock price synchronicity, while network centrality improves it. Mechanism analysis suggests that the information network affects stock price synchronicity through information diffusion and spillover effects. Furthermore, the analysis shows that the impact of information network structure on stock price synchronicity varies under different market conditions. The findings of this study provide valuable insights for strengthening the supervision of institutional investors’ behavior and reducing stock price synchronicity in emerging markets.

Urban Texture Identification and Characteristic Analysis Based on Percolation Theory—A Case Study of the Second Ring Road Area in Wuhan City

The urban texture is the physical manifestation of the urban form’s evolution. In the rapid process of urbanization, protecting and reshaping the urban texture has become an essential means to sustain the overall form and vitality of cities. Previous studies in this field have primarily relied on image analysis or typological methods, lacking a quantitative approach to identify and analyze the urban texture on a large scale. Moreover, the hierarchical structure and networked organization of the urban texture are gradually being elucidated and emphasized. This study takes a complex network perspective and applies percolation theory to identify and analyze the urban texture. The hierarchical evolutionary characteristics of the urban fabric and the underlying mechanisms driving the scale-dependent differences are analyzed, and the toughness of the urban texture is evaluated based on hierarchical connections. The research findings reveal the key scale in the cross-scale evolution of urban textures, with variations in scale dependence and hierarchical evolution characteristics among different types of urban texture. The traditional urban texture displays sensitivity to scale changes, maintaining its integrity and internal connectivity at small scales. On the other hand, the texture characteristics of modern and contemporary urban areas are only discernible at larger scales. The emergence of large-scale texture clusters at specific scale levels can be explained using multiple index systems. This study has reference significance for the preservation and renewal of the urban fabric in urban renewal processes.

Analysis of Global Core Countries from a Complex Network Perspective

This paper analyzes the importance of countries in a globalized world and explores their cooperative tendencies using a complex network approach. By extracting data from The GDELT Project, an undirected weighted global network is constructed, where each node represents a country. The network is found to be scale-free based on the degree distribution. Core countries are identified using different centrality metrics, such as degree centrality, closeness centrality, and PageRank. The occurrence of war is identified as a significant factor influencing the importance of countries like Israel and Ukraine in the network. By considering the types of events, the study examines the cooperative tendencies of countries and finds an overall trend of cooperation rather than conflict among nations, with varying levels of collaboration. Countries involved in conflicts or wars show limited cooperation with other nations during the selected period. The findings suggest that globalization may influence the cooperative tendencies of countries. The paper contributes to the understanding of the importance of countries in a globalized world and their cooperative tendencies using a complex network perspective

A complex network perspective on spatiotemporal propagations of extreme precipitation events in China

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.

Spatiotemporal evolution patterns and underlying formation mechanisms of monsoon rainfall across eastern China: A complex network perspective

Quantifying the evolution patterns and forming causes of monsoon rainfall is essential for prediction and mitigation of resultant hydrometeorological hazards. This study proposed a complex network-based framework to investigate these aspects over eastern China throughout the monsoon season. Based on the extracted heavy precipitation events (HPEs) at each grid within the monsoon season and twenty segmented phases, event synchronization and complex networks were utilized to construct HPE synchronized networks and HPE delayed networks, respectively. The former were utilized to identify monsoon rainfall subregions over eastern China, and further investigate the evolution rules of monsoon rainfall and associated hazard risks across the monsoon season. The latter were employed to investigate the spatial propagation patterns of monsoon rainfall within eastern China and individual subregions during their respective rainy seasons. Furthermore, the driving mechanisms behind the spatial propagation of monsoon rainfall within various subregions were explored by diagnosing the typical spatial propagation periods of monsoon rainfall and relevant regional atmospheric circulation anomalies. Results show that there are ten monsoon rainfall subregions over eastern China. Across these subregions, the monsoon rainfall undergoes seven sequential stages, including three stationary periods, two abrupt northward jumps and one southward retreat. As phases progress, zones with high hazard risks migrate within South China (SC), Yangtze River Basin (YRB), and Liaohe River Basin (LRB) with area first increasing and then decreasing from mid-April to early August. Diverse spatial propagation patterns of monsoon rainfall within various subregions arise from variations in the surrounding atmospheric circulation systems, resulting in anomalous wind patterns and moisture distribution. These anomalies, in turn, foster favorable conditions for monsoon rainfall to propagate along anomalous water vapor transport pathways. These findings advance our understanding of monsoon systems and associated hazard risks, shedding new light on their predictability.

Uncovering the endogenous features of potassium salts' global transfer: A complex network perspective.

Potassium is a decisive strategic resource to ensure food safety production and supply, which many nations define as a critical metal. Due to the unbalanced distribution of resources and production capacity and the separation of the primary potassium-consuming and supplying countries, international trade is the main supply channel for potassium-consuming countries to acquire enough resources. Understanding the characteristics of potassium trade networks and the evolution of trade patterns is essential for supply security. To explore this issue, this paper employs the complex network theory to quantitatively analyze the evolution characteristics of the global potassium trade network (PTN) from 2000 to 2021. The results show (1) Overall, the potassium trade shows a trend of gradual prosperity, efficiency, and concentration; (2) During the two decades, the main exporting countries of potassium remained stable, while imports changed significantly; (3) The evolution of the potassium trade community has characterized the fragmentation-regionalization-high concentration over time; (4) The trade flow of PTN is unbalanced, and few countries show outstanding capabilities but a single function. These findings would help trade policymakers manage the supply of strategic raw materials more effectively.

Systematic Risk Analysis of Listed Financial Institutions from the Perspective of Complex Network

Based on the complex network theory, this paper studies the systemic financial risks in China’s financial market. According to the industry classification of the China Securities Regulatory Commission in 2012, the daily closing prices of 45 listed financial institutions are collected and the daily return rates of each financial institution are measured according to the logarithmic return rate calculation formula. In this paper, the risk spillover value ΔCoVaR is used to measure the contribution degree of each financial institution to systemic risk. Finally, the relationship between the risk spillover value ΔCoVaR and the node topology index of the risk transmission network is investigated by using a regression model, and some policy suggestions are put forward based on the regression results.

Analysis of a Multiple Traffic Flow Network’s Spatial Organization Pattern Recognition Based on a Network Map

Detecting the spatial organization patterns of urban networks with multiple traffic flows from the perspective of complex networks and traffic behavior will help to optimize the urban spatial structure and thereby promote the sustainable development of the city. However, there are notable differences in regional spatial patterns among the different modes of transportation. Based on the road, railway, and air frequency data, this article investigates the spatial distribution and accessibility patterns of multiple transportation flows in the Yangtze River Economic Belt. Next, we use the TCD (Transportation Cluster Detection) community discovery algorithm and integrate it with the Baidu Maps API to obtain real-time time cost data to construct a community detection model of a multiple traffic flow network. We integrate the geographical network and topological network to perform feature extraction and rule mining on the spatial organization model of the urban network in the Yangtze River Economic Belt. The results show that: (1) The multiple traffic flow network of the Yangtze River Economic Belt has significant spatial differentiation. The spatial differentiation of aviation and railway networks is mainly concentrated between regions and within provinces, while the imbalance of highway networks is manifested as an imbalance within regions and between provinces. (2) The accessibility pattern of the highway network in the Yangtze River Economic Belt presents a “core–edge” spatial pattern. The accessibility pattern of the railway network generally presents a spatial pattern of “strong in the east and weak in the west”. Compared with sparse road and railway networks, the accessibility pattern of the aviation network shows a spatial pattern of “time and space compression in western cities”. (3) A total of 24 communities were identified through the TCD algorithm, mainly encompassing six major “urban economic communities” located in Guizhou, Yunnan, Anhui, Sichuan–Chongqing, Hubei–Hunan–Jiangxi, and Jiangsu–Zhejiang–Shanghai. (4) The urban network space organization model of the Yangtze River Economic Belt can be roughly divided into three models: the “single-core” model, with Guizhou, Kunming, and Hefei as the core, the “dual-core” model, constructed by Chengdu–Chongqing, and the “multi-core” model, constructed by Changsha–Wuhan–Nanchang and Shanghai–Nanjing–Hangzhou. This model of urban network spatial organization holds indicative significance in revealing the spatial correlation pattern among prefecture-level city units.

Analysis of tourism destination centrality and structural properties of tourism system: Complex network perspective

ourism has become a new way of living with the living standard development. This study analyses the tourism destination centrality and spatial patterns of the tourism system using complex network analysis. An analysis of 245 destinations in the South of Thailand has found that the network has a low network density, large average path length and low clustering coefficient. Some a small number of high-degree destinations connect to each other, while most connect to others with a low degree. The network comprises 18 subnetworks that destinations densely connect to others in the same subnetwork but sparsely connect to others in different ones. Destinations play different roles in the network based on which a centrality measure is used, degree, betweenness and closeness centrality. 31 destinations with high hub and authority centrality are the centers playing as hubs of the network. The study’s findings draw implications for the sector.

EEG-Based Brain Functional Network Analysis for Differential Identification of Dementia-Related Disorders and Their Onset.

Diagnosing and treating dementia, including mild cognitive impairment (MCI), is challenging due to diverse disease types and overlapping symptoms. Early MCI detection is vital as it can precede dementia, yet distinguishing it from later stage dementia is intricate due to subtle symptoms. The primary objective of this study is to adopt a complex network perspective to unravel the underlying pathophysiological mechanisms of dementia-related disorders. Leveraging the extensive availability of electroencephalogram (EEG) data, our study focuses on the meticulous identification and analysis of EEG-based brain functional network (BFNs) associated with dementia-related disorders. To achieve this, we employ the Phase Lag Index (PLI) as a connectivity measure, offering a comprehensive view of neural interactions. To enhance the analytical rigor, we introduce a data-driven threshold selection technique. This innovative approach allows us to compare the topological structures of the formulated BFNs using complex network measures quantitatively and statistically. Furthermore, we harness the power of these BFNs by utilizing them as pre-defined graph inputs for a Graph Convolution Network (GCN-net) based approach. The results demonstrate that graph theory metrics, such as the rich-club coefficient, transitivity, and assortativity coefficients, effectively distinguish between MCI, Alzheimer's disease (AD) and vascular dementia (VD). Furthermore, GCN-net achieves high accuracy (95.07% delta, 80.62% theta) and F1-scores (0.92 delta, 0.67 theta), highlighting the effectiveness of EEG-based BFNs in the analysis of dementia-related disorders.

A hybrid causal machine learning to reveal driving factors responsible coal market: Case of the Chinese industry

To achieve carbon neutrality, China must grasp the intricate factors driving coal market, as it addresses coal consumption and reduces the carbon footprint. While previous studies have identified certain factors, they have largely overlooked the impact of the overall industry sector on the coal market. In this paper, a hybrid causal machine learning model was proposed that combine with least absolute shrinkage and selection operator (LASSO), multiple linear regression (MLR), peter and clark momentary conditional independence (PCMCI) and interpretable machine learning. The approach employed a complex network perspective to identify 10 key factors in China's coal market from three dimensions: industrial sector, foreign trade and energy market. The hierarchical pattern of these factors is classified into two levels: direct factors, primarily encompassing crude oil futures, exchange rates and architectural decoration, and intermediary factors, mainly including the chemical industry and building material. Interestingly, this paper reaffirms the roles of crude oil futures and exchange rates in the coal market, as identified in prior studies. Additionally, it uncovers new influential factors primarily driven by the chemical industry and architectural decoration, previously unexplored. Furthermore, this paper found a significant contemporaneous relationship between crude oil futures and coal price index (0.151) as well as coal consumption (0.102), and the strong relationship between coal consumption and the Indonesian Rupiah to Chinese Yuan exchange rate (0.063) was also confirmed. Among the emerging dominant factors, the coal price index and the chemical industry (0.145), along with coal consumption and building materials (0.250), also exhibit a strong contemporaneous relationship. These findings highlight the importance of considering the entire industry sector while analyzing the coal market and promoting a cleaner transformation of the industry in China, and the paradigm presented not only applicable to the coal market, but can be extended to other industries.

Assessing road network resilience in disaster areas from a complex network perspective: A real-life case study from China

Recently, resilience assessment has become a significant research topic, as it is crucial for regional disaster mitigation. It is important to identify road network resilience characteristics to improve regional traffic and handling capacity during emergencies, which ensures regional transport safety management and sustainable development. In this paper, a complex-network-based resilience assessment framework coupled with resilience curve characteristics, such as vulnerability, survivability, adaptability, responsiveness and resilience, was established. Typical Chinese regions with the most extensive disaster distribution were selected to reproduce real historical disaster scenarios for comparative analysis and research. The results showed that 1) the process of the change in the resilience of the road network during a disaster conforms to the characteristics of the resilience curve; 2) dense, homogeneous networks are more resilient than sparse, clustered networks; 3) road sections with more redundancy and substitutability are more resilient; and 4) the severity of the decline in traffic network performance in disaster areas is related to the attributes of road segments and network interference methods.

A study of the effect of influential spreaders on the different sectors of Indian market and a few foreign markets: a complex networks perspective

A study of the effect of influential spreaders on the different sectors of Indian market and a few foreign markets: a complex networks perspective

Migration Networks Pattern of China’s Floating Population from the Perspective of Complex Network

Migration Networks Pattern of China’s Floating Population from the Perspective of Complex Network

Evaluating the Connectivity and Imbalance Contribution of New Sections towards Highway Network: A Complex Network Perspective

The evaluation of the impacts of new sections on the highway network is an essential aspect of the feasibility study. Existing studies predominantly concentrated on engineering-oriented feasibility assessments, often overlooking their potential effects on parallel sections and the overall network. In this research, we present an evaluation model for new sections based on complex networks, focusing on the connectivity and imbalance of transportation networks. This model serves as a supplementary approach for enhancing the feasibility analysis of new highway projects. The model comprises three distinct modules, namely, complex network, eigenvalue, and evaluation. Therein, the complex network provides diverse attributes for sections with the dynamic edge weights. Moreover, probability betweenness centrality and volume betweenness centrality have been presented as an eigenvalue of sections based on the multilayer complex network. Furthermore, the connectivity evaluation based on the eigenvalue and the imbalance evaluation based on the entropy and Gini coefficient are conducted. Through the case study, the results of the model demonstrate the connectivity and imbalance contribution of new sections and provide a novel perspective for the feasibility study.

Centrality in the macroeconomic multi-network explains the spatiotemporal distribution of country per-capita income

This paper empirically investigates the role played by cross-country spillovers in shaping spatiotemporal differences in country income. While existing literature focused on effects captured by direct spillovers with partner countries only, here we take a complex network perspective to explore whether the global embeddedness of countries in the macroeconomic multi-network may significantly impact income, net of country local characteristics such as local foreign exposure. We employ data for the period 2000–2020 to build a time sequence of 3-layer multi graphs, with countries as nodes and links weighted by the intensity of bilateral relations in international trade, finance and human migration. Using panel-regression techniques, we then ask if country (eigenvector) centrality in the multi network can account for parts of the observed heterogeneity in country per-capita income, both cross-sectionally and over time. Robustly across a number of alternative specifications of the empirical model, we find that being more central significantly boosts country income. This implies that income-enhancing technological spillovers are not only channeled via local exposure, but also through indirect interactions with more distant nodes.