Complex Network Approach Research Articles

Living High-Training Low on Mice Bone Parameters Analyzed through Complex Network Approach.

The aim of this study was to investigate the effect of 8 weeks of hypoxic exposition and physical training on healthy mice femur outcomes analyzed through conventional statistic and complex networks. The mice were divided into four groups, subjected to physical training (T; 40 min per day at 80% of critical velocity intensity) or not (N), exposed to hypoxic environment ("Living High-Training Low" model - LHTL; 18 h per day, FIO2=19.5%; Hyp) or not (Nor). The complex network analysis performed interactions among parameters using values of critical "r" of 0.5 by Pearson correlations to edges construction, with Fruchterman-Reingold layout adopted for graph visualization. Pondered Degree, Betweenness, and Eigenvector metrics were chosen as centrality metrics. Two-way ANOVA, t-test and Pearson correlation were used with P<0.05. Femur phosphorus of T-Hyp was higher than all other groups (P<0.05) and correlated with bone density (r=0.65; P=0.042), bone mineral density (r=0.67; P=0.034) and% of mineral material (r=0.66, P=0.038). Overall, the complex network demonstrated improvements in bone volume, % of mineral material, bone density, and bone mineral density for T-Hyp over other groups. Association of physical training and hypoxia improved bone quality for healthy mice.

Managerial interlocking networks and firm risk spillover: evidence from China

PurposeThis paper examines the effect of managerial interlocking networks (MINs) on firm risk spillover by using a sample of Chinese A-share listed firms.Design/methodology/approachApplying the complex network approach, we build managerial interlocking networks (MINs) and leverage degree centrality to quantify a manager’s network position. To gauge firm risk spillover, we utilize the conditional autoregressive value at risk (CAViaR) model to compute the value-at-risk. Subsequently, we employ ordinary least squares to investigate the influence of MINs on firm risk spillover.FindingsOur research uncovers a direct correlation between a firm risk spillover and the status of network positions within managerial interlocking networks; namely, the more central the position, the greater the risk spillover. This increase is believed to be due to central firms in MINs having greater connectedness and influence. This fosters a similarity in decision-making across different firms through interfirm managerial communication, thus amplifying the risk spillover. Economic policy uncertainty (EPU) and Guanxi culture furtherly intensify the effects of MINs. Additional analysis reveals that the impact of MINs on the firm risk spillover is significantly noticeable in non-state-owned enterprises, while good corporate governance diminishes the risk spillover prompted by MINs.Originality/valueOur findings offer fresh insights into the interfirm risk outcome associated with MINs and extend practical guidelines for attenuating firm risk spillover with a view toward mitigating systemic risk.

Analysing Sector Safety Performance Based on Potential Conflicts: Complex Network Approach

This paper presents an in-depth analysis of Sector Safety Performance (SeSPe), focusing on potential conflicts within the Spanish Air Traffic Network. SeSPe serves as a comprehensive analysis for evaluating the safety levels of individual airspace sectors, taking into account both the frequency and severity of potential conflicts, along with their geospatial characteristics. Using Complex Network Theory, this study applies a novel methodology to four Madrid ACC sectors, analysing one month of flight track data. Multiple weighted spatial-temporal networks are built using 60 min intervals, allowing for the identification of critical nodes and the examination of interconnections and structural dynamics within the air traffic network. By incorporating temporal variations, the analysis uncovers evolving patterns. The research introduces an innovative approach for calculating potential conflicts by integrating radar data with airspace structure, thus offering a deeper understanding of sector risk profiles. The development of the SeSPe indicator, which combines historical safety data with topological features, enables a more informed and strategic evaluation of Air Traffic Management (ATM) system performance, ultimately contributing to safer and more efficient airspace operations.

Unveiling the dynamic evolution of innovation networks in emerging economies: A complex network approach

AbstractThe innovation network of enterprises in emerging economies is a complex system, comprising numerous interconnected elements. In the context of a dynamic and volatile market, innovation entities evolve the evolution of the local world through collaborative innovation and interactions. To accurately replicate the actual dynamics of the enterprise innovation network, an in‐depth analysis is essential to delineate the growth and reduction mechanisms of diverse nodal entities. This research, based on the classical complex network paradigm, proposes a model that is based on preferential attachment and pruning mechanisms. This model reflects the evolutionary trajectory of enterprise innovation networks and was subjected to simulation analysis using MATLAB. The findings prove that simulation is an effective analytical tool for understanding the evolutionary mechanisms of innovation networks within emerging economies. The model clearly demonstrates the complex evolution of enterprise innovation networks, providing both theoretical foundations and practical guidance for strategic resource allocation and network governance within corporate environments.

Cascading spatial drought network: A complex networks approach to track propagation of meteorological droughts to agricultural droughts

Meteorological droughts often propagate to agricultural (and other) droughts, both spatially and temporally. The present study proposes a novel complex networks-based cascading spatial drought network to examine the spatial propagation of meteorological droughts in a region to agricultural droughts in other regions. This is done through: (1) establishing stable homogeneous drought communities; (2) investigating inter-community drought propagation; (3) locating drought sources; and (4) evaluating drought connections within major crop belts. The approach is implemented to study droughts in the Indian-subcontinent during the period 1948–2022. Monthly precipitation and root-zone soil moisture data from GLDAS (Global Land Data Assimilation System) are used to compute the standardized precipitation index (SPI) for meteorological droughts and standardized soil moisture index (SSI) for agricultural droughts. Primarily, the drought network is demarcated into several subsets of network communities within which clusters of localized propagation take place. Multi-community subgraphs combining different communities are also formed to understand the long-distance inter-community drought linkages. Using network centrality measures, such as degree, closeness, and clustering coefficient, network properties of scale-freeness, small-worldness, and presence of rich-clubs are checked. Although the overall network does not exhibit any of these properties, certain subgraphs have significant small-worldness, rich-clubs, and partial scale-freeness. Some of the crucial nodes that support these network properties lie in the monsoon pathways (in the Western Ghats), and others have a strong association with El Niño Southern Oscillation (ENSO) teleconnections, thus validating the ability of the drought network to capture seasonal and climatic features. Additionally, subgraphs of nodes with high productivity of different food crops are created to study drought propagation within crop belts. Barring potential shortcomings related to data dependencies, the cascading spatial drought network helps identify an impending agricultural drought that could strengthen our ability to forecast droughts.

Resilience evaluation and enhancing for China’s electric vehicle supply chain in the presence of attacks: A complex network analysis approach

Over the past decade, the electric vehicle (EV) industry has experienced rapid growth, necessitating a deeper understanding of its supply chain network’s structure and resilience. This study presents an analysis into China’s EV supply chain network through a two-tier network, employing complex network approach to explore its topological characteristics and network’s resilience. The results reveal that the nodes within China’s EV network follow a power-law distribution, with shorter average path lengths compared to other automobile networks, indicating a concentration of connections upstream. The resilience assessment shows that while the network is resilient against random attacks, it is significantly vulnerable to targeted attacks. Importantly, the High-Betweenness Addition (HBA) strategy is identified as a better strategy for improving the network’s overall resilience. This strategy of reinforcing the centrality of core nodes within the supply chain is highlighted as a crucial approach for enhancing the network’s resilience. Our findings underscore the importance of developing strategic connections among high-betweenness nodes to distribute these enhancements evenly, aiming to add one or two edges for enhancing network resilience. This study offers a lucid roadmap for enhancing the resilience of China’s EV supply chain network in the face of emerging challenges.

The construction and theoretical investigation of compound-protein target-pathway network for Radix Pueraria

The complex network approach is an effective method to analyze the global properties of complex biological systems, which can be used to explore the interactions between multiple compounds and protein targets of drug. Radix Pueraria has a long history of efficacy in different diseases, containing a variety of compounds that can produce multiple therapeutic effects via multiple targets. To explore the interrelationship between compounds of Radix Pueraria, protein targets, and pathways, the present investigation used the complex network approach to construct the compound-protein target-pathway network of Radix Pueraria. The analysis of the statistical and topological characteristics shows that the network has small-world and scale-free characteristics. The diameter of the network is 7, indicating that the information exchange between two nodes could quickly spread to the whole network. With the rank of degree value and integrated centrality as identification indicators, by taking the threshold of integrated centrality as 0.70, a total of nine key hub nodes containing three active compounds, two protein targets, and four biological pathways were identified: genistein, daidzein, puerarin, MAPK3, MAPK1, hsa01100, hsa05200, hsa05417, and hsa05207. The network analysis suggests these key hub nodes should play an important role in the pharmacological action of Radix Pueraria. The results of this study provide useful information and knowledge for further exploring the pharmacological mechanism of Radix Pueraria in the course of disease treatment.

Can Bayesian Networks Improve Ground-Strike Point Classification?

Studying cloud-to-ground lightning strokes and ground-strike points provides an alternative method of lightning mapping for lightning risk assessment. Various k-means algorithms have been used to verify the ground-strike points from lightning locating systems, producing results with room for improvement. This paper proposes using Bayesian networks (BNs), a model not previously used for this purpose, to classify lightning ground-strike points. A Bayesian network is a probabilistic graphical model that uses Bayes’ theorem to represent the conditional dependencies of variables. The networks created for this research were trained from the data using a score-based structure-learning procedure and the Bayesian information criterion score function. The models were evaluated using confusion matrices and kappa indices and produced accuracy values ranging from 86% to 94% and kappa indices of up to 0.76. While BN models do not outperform k-means algorithms, they offer an alternative by not requiring predetermined distances. However, the easy implementation of the k-means approach means that no significant gain is made by implementing the more complex Bayesian network approach.

Complex Network View of the Sun’s Magnetic Patches. I. Identification

Solar and stellar magnetic patches (i.e., magnetic fluxes that reach the surface from the interior) are believed to be the primary sources of a star’s atmospheric conditions. Here, we apply the complex network approach and investigate its efficacy in the identification of these features. For this purpose, we use the line-of-sight magnetograms provided by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. We construct the magnetic network following a specific visibility graph condition between pairs of pixels with opposite polarities and search for possible links between these regions. The complex network facilitates the construction of node degrees and PageRank images, and applying the downhill algorithm to node-degree images allows for the grouping of pixels into features corresponding to one-to-one matches with magnetogram patches. This approach promisingly serves to identify the nontrivial morphological structure of the magnetic patches for small and large sizes. We observe that the changes in the features of the node-degree images effectively correspond to the cospatial magnetic patches over time. Through visual assessment, we estimate an average false-negative error rate of approximately 1% in identifying small-scale features (one or two pixels in size).

Evolution, resilience and causes of global petroleum gas trade networks: 1995–2020

Based on the HS 4-digit code trade data in UNCOMTRADE from 1995 to 2020, this paper analyzes the characteristics of the evolution of the global PG trade network using the complex network approach and analyzes the changes in its resilience at the overall and country levels, respectively. The results illustrated that: (1) The scale of the global PG trade network tends to expand, and the connection is gradually tightened, experiencing a change from a “supply-oriented” to a “supply-and-demand” pattern, in which the U.S., Russia, Qatar, and Australia have gradually replaced Canada, Japan, and Russia to become the core trade status, while OPEC countries such as Qatar, Algeria, and Kuwait mainly rely on PG exports to occupy the core of the global supply, and the trade status of other countries has been dynamically alternating and evolving. (2) The resilience of the global PG trade network is lower than that of the random network and decreases non-linearly with more disrupted countries. Moreover, the impact of the U.S. is more significant than the rest of countries. Simulations using the exponential random graph model (ERGM) model revealed that national GDP, institutional quality, common border and RTA network are the determinants of PG trade network formation, and the positive impact of the four factors not only varies significantly across regions and stages, but also increases with national network status.

What does the language system look like in pre-trained language models? A study using complex networks

Pre-trained language models has advanced the fields of natural language processing. The exceptional capabilities exhibited by PLMs in NLP tasks have been attracting researchers to explore the underlying factors responsible for their success. However, most of work primarily focus on studying some certain linguistic knowledge encoded in PLMs, rather than investigating how these models comprehend language from a holistic perspective. Furthermore, they cannot point out how PLMs organize the whole language system. Therefore, we adopt the complex network approach to represent the language system, and investigate how language elements are organized within the system. Specifically, we take the attention relationships among words as the research object, which are generated by attention heads within BERT models. Then, the words are treated as nodes, and the connections between words and their most-attending words are represented as edges. After obtaining these "words' attention networks", we analyze the network properties from various perspectives by calculating the network metrics. Many constructive conclusions are summarized, including: (1) The English attention networks demonstrate exceptional performance in organizing words; (2) Most words’ attention networks exhibit small-world property and scale-free behavior; (3) Some networks generated by multilingual BERT can reflect typological information well, achieving preferable clustering performance among language groups; (4) In cross-layer analysis, the networks from 8 to 10 layers in Chinese BERT and from 6 to 9 layers in English BERT exhibit more consistent characteristics. Our study provides a comprehensive explanation of how PLMs organize language systems, which can be utilized to evaluate and develop improved models.

Does the limelight foster the green transition of listed industrial companies? An empirical analysis from Chinese A-share market

This paper contributes current research by investigating the extent to which equity linkage networks impact enterprise green transition in sustainable development. By adopting a complex network approach, we constructed a common shareholding network based on the top ten shareholders of listed industrial enterprises in Shanghai and Shenzhen A-shares from 2013 to 2022. The empirical results indicate that enterprises closer to the centre of the equity linkage network tend to have higher degrees of green transition. This impact is facilitated through three mechanism channels of capital flow, information exchange, and knowledge transfer within the network. We find enterprises at the core of the network can effectively reduce their financing constraints on enterprises, mitigate risks associated with external environmental uncertainties and managerial myopia, and promote knowledge exchange and innovation cooperation between enterprises. We have also discovered that the centrality of equity network has a greater impact on promoting transition in state-owned, large-scale enterprises, and enterprises with less heavy pollution and the network effect can be enhanced by strong regional environmental regulations. The above findings not only provide policy makers with policy recommendations to guide the enterprises green transition, but also provide industry practitioners with practical paths and directions, which can help promote the green development process of the whole society.

Income inequality analysis through complex network and nonlinear time series approaches: an Econophysics perspective

Income inequality analysis through complex network and nonlinear time series approaches: an Econophysics perspective

The influence patterns of carbon flux in different climatic zones in China —Based on the complex network approach

Research on ecosystem carbon flux can provide important methodological and strategic support for climate change mitigation. The existing studies focus on the calculation of carbon flux, ignoring the intertwined effects between regions. The quantification and analysis of the interaction patterns of carbon flux is crucial for understanding the global carbon cycle process, forecasting and coping with climate change. In this study, carbon flux network model sequences are established based on complex network theory using carbon flux data spanning from December 1, 2005, to November 30, 2020. The time delay effect is introduced to accurately quantify the influence patterns of carbon flux within climate zones across China. The findings indicate that the probability distribution function of the link weights during the various seasons of each year exhibits a bimodal distribution with distinct positive and negative components. The delay probability distribution function reveals the significant impact of delay effects, which are especially pronounced and mostly significant long-term lag effects in nodes with negative weights. Further, the results of the interactions of carbon flux among climate zones demonstrate that changes in carbon flux in the plateau and southern temperate regions have either positive or negative impacts on other climate zones. Therefore, controlling carbon flux changes in these climatic zones can effectively optimize the distribution of carbon flux. The modeling framework and results presented in this paper provide useful insights for the regulation and distribution optimization of carbon flux in China.

A complex network approach to analyse pre-trained language models for ancient Chinese.

Ancient Chinese is a splendid treasure within Chinese culture. To facilitate its compilation, pre-trained language models for ancient Chinese are developed. After that, researchers are actively exploring the factors contributing to their success. However, previous work did not study how language models organized the elements of ancient Chinese from a holistic perspective. Hence, we adopt complex networks to explore how language models organize the elements in ancient Chinese system. Specifically, we first analyse the characters' and words' co-occurrence networks in ancient Chinese. Then, we study characters' and words' attention networks, generated by attention heads within SikuBERT from two aspects: static and dynamic network analysis. In the static network analysis, we find that (i) most of attention networks exhibit small-world properties and scale-free behaviour, (ii) over 80% of attention networks exhibit high similarity with the corresponding co-occurrence networks, (iii) there exists a noticeable gap between characters' and words' attention networks across layers, while their fluctuations remain relatively consistent, and (iv) the attention networks generated by SikuBERT tend to be sparser compared with those from Chinese BERT. In dynamic network analysis, we find that the sentence segmentation task does not significantly affect network metrics, while the part-of-speech tagging task makes attention networks sparser.

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

Complex networks approach to study comorbidities in patients with unruptured intracranial aneurysms

The role of complex network analysis in patients with diagnosis of unruptured intracranial aneurysm is unexplored. The objective of this study is to assess the applicability of this methodology in aneurysm patients. We retrospectively analyze comprehensive unbiased local digital data of a large number of patients treated for any reason between January 2004 and July 2019. We apply an age-cohort approach to a total of 628,831 patients and construct the diagnostic history of each patient—and include the information how old the patient was when diagnosed for the first time with each diagnosis coded according to International Classification of Diseases. For each cohort of age within a 10 year interval and for each gender, we construct a statistically validated comorbidity network and focused on crucial comorbidity links that the aneurysm code has to other disease codes within the whole network. For all cohorts of different age and gender, the analysis shows that 267 diagnose codes have nearest neighbour statistically validated links to unruptured aneurysm ICD code. Among the 267 comorbidities, 204 (76%) were found in patients aged from 40 to 69-years old. Patterns of connectivity with aneurysms were found for smoking, hypertension, chronic obstructive pulmonary disease, dyslipidemia, and mood disorders. A few uncommon connections are also detected in cohorts of female patients. Our study explored the applicability of network analysis and statistical validation in aneurysm observational study.

Correlation Analysis of Stock Markets Along the Belt and Road: A Generalised Complex Network Approach

Correlation Analysis of Stock Markets Along the Belt and Road: A Generalised Complex Network Approach

Conceptualizing Forest Operations Planning and Management Using Principles of Functional Complex Systems Science to Increase the Forest’s Ability to Withstand Climate Change

The sustainable management of forest resources is greatly influenced by forest operations (FO). Interactions between humans and nature describe how people engage with and are impacted by the natural world. As we enter the Anthropocene epoch, we are being compelled to reevaluate our past and present methods of managing and planning our forest operations in order to find new ones that are more adaptable and successful at addressing the growing unpredictability resulting from accelerating global change. We briefly discuss the goals and constraints of the prior and current management and planning principles for forest operations in this study, focusing on how these principles have evolved on a worldwide scale. We then propose a promising idea, such as managing forest operations as complex adaptive systems and approaches based on resilience and sustainable use of forest resources, in order to achieve the necessary economic, social, and ecological goals. An in-depth understanding of the ecological, economic, and social factors that influence forest resilience is necessary for planning and managing forest operations efficiently. The proposed strategy combines the effectiveness of forest operations with a functional, complex network approach in order to manage forests for the Anthropocene.

Three Decades of Oasis Transition and Its Driving Factors in Turpan–Hami Basin in Xinjiang, China: A Complex Network Approach

As a predominant ecosystem-providing area and distinctive landscape in arid regions, an oasis plays an important role in maintaining land stability, human production, and daily activities. Studying the dynamics of oasis and its driving factors is vital to supporting arid regions’ sustainable development. As a typical mountain–desert–oasis landscape, the Turpan–Hami (Tuha) Basin, located in Xinjiang, China, includes complex interactions among different land types. For this study, we revealed the spatio-temporal patterns and transition processes of the oasis using a complex network method between 1990 and 2020 in the Tuha Basin. In the oasis transition network, the degree value, betweenness centrality, and average path length were calculated to express the transition relationship, key oasis type, and oasis structural stability, respectively. Six factors related to climate change and human actives were selected to investigate the driving forces behind oasis transitions, including the average temperature and precipitation in the growing season, the total power of agricultural machinery (TAMP), the production of raw coal (PRC), the total output value of the plantation industry (TPI), and the population (Pop). Our results show that the oasis area of the Tuha Basin, including the natural oasis and artificial oasis, all grew from 1990 to 2020, with the natural oasis expanding more than the artificial oasis. The transitions between oasis types became more frequent as the area of oasis land types increased throughout the study period. Grassland acted as the most important oasis type in the network, with the highest betweenness centrality, but its importance declined due to the increasing complexity of the oasis transition network from 1990 to 2020. The transitions between oasis types became simpler, and the oasis structural stability decreased. Through driving force analysis, the oasis changes showed a positive correlation with the temperature (p-value &lt; 0.05, r = 0.88), and urbanization and industrialization factors prompted transitions to built-up areas and cropland from grassland and shrubland. In summary, our results suggest that to create a harmonious symbiotic relationship between the natural environment in dryland and human activities, preventing grassland degradation and excessive reclamation of land cover is an available way. Meanwhile, the protection of shrubland and water resources is also important. This study provided reference and theory support for promoting the sustainable development of oases.