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The input–output (IO) model can be used to examine the flow of products and services within an economy, resembling a network with industries as nodes and transactions as links. Diverging significantly from commonly studied networks such as social, protein, and power grids, IO networks exhibit intricate interconnectivity, involving weighted nodes and both directional and weighted links. This uniqueness necessitates careful consideration when applying complex network analysis techniques to IO systems. We critically review current complex network metrics and attempt to link them with existing IO approaches. Based on our assessment, certain network metrics, such as degree centrality and eigenvector centrality, have been explicitly integrated into the IO theory. In contrast, there exist metrics whose definitions and interpretations expand when applied in the context of IO analysis, including closeness and betweenness centrality. Additionally, network metrics are usually used to study topological features, identify key sectors, and construct novel metrics to study related issues. Network metrics used in IO analysis can identify important driver and transmission sectors in resource flow and environmental emission network, facilitating the development of targeted and reliable strategies. Besides, network metrics are used to quantify topological features and structural changes of the IO network which help strengthen the supply chain and mitigate both direct and indirect impacts of disruptions. Our ultimate goal is to establish connections and offer a roadmap for developing network‐based tools in IO analysis. [ABSTRACT FROM AUTHOR]

• We investigate whether national boundaries still matter in the global diffusion of innovation. • We build up a new longitudinal global network of product-embodied R&D flows between industries. • We use complex network analysis to investigate the relevance of national boundaries in this network. • Results hint national innovation systems play an important role in this global network. We investigate the extent to which national boundaries still matter in the diffusion of innovation globally. By combining the World Input-Output Dataset with OECD data on Analytical Business Enterprise R&D, we built a longitudinal network of 690 nodes (23 industries in 30 countries) between 2009 and 2013, mapping product-embodied R&D flows between industries within and across countries worldwide. Our complex network analysis examines the characteristics of the network and the relevance of national boundaries. It suggests that, while, over time, the diffusion of product-embodied R&D has extended to include more countries and more industries, the importance of exchanges amongst national industries persists. Industries in specific countries are responsible for essential discontinuities in terms of their centrality in the global network, while industries tend to gather in within national boundaries. It would seem that, despite some cross-country variability, within-country flows are more relevant than international flows in relation to the structural features of the global network nodes. These findings suggest the continued significance of national innovation systems for the development and diffusion of innovation. [ABSTRACT FROM AUTHOR]

Background: Low air quality related to ambient air pollution is the largest environmental risk to health worldwide. Interactions between air pollution emissions may affect associations between air pollution exposure and chronic diseases. Therefore, this study aimed to quantify interactions among air pollution emissions and assess their effects on the association between air pollution and diabetes. Methods: After constructing long-term emission networks for six air pollutants based on data collected from routine monitoring stations in Northeast China, a mutual information network was used to quantify interactions among air pollution emissions. Multiple linear regression analysis was then used to explore the influence of emission interactions on the association between air pollution exposure and the prevalence of diabetes based on data reported from the Northeast Natural Cohort Study in China. Results: Complex network analysis detected three major emission sources in Northeast China located in Shenyang and Changchun. The effects of particulate matter (PM2.5 and PM10) and ground-level ozone (O3) emissions were limited to certain communities but could spread to other communities through emissions in Inner Mongolia. Emissions of sulfur dioxide (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO) significantly influenced other communities. These results indicated that air pollutants in different geographic areas can interact directly or indirectly. Adjusting for interactions between emissions changed associations between air pollution emissions and diabetes prevalence, especially for PM2.5, NO2, and CO. Conclusions: Complex network analysis is suitable for quantifying interactions among air pollution emissions and suggests that the effects of PM2.5 and NO2 emissions on health outcomes may have been overestimated in previous population studies while those of CO may have been underestimated. Further studies examining associations between air pollution and chronic diseases should consider controlling for the effects of interactions among pollution emissions. [ABSTRACT FROM AUTHOR]

With the changes in tuna supply and demand pattern, trade policy, resource endowment and other factors in the world, the tuna trade relationship is undergoing a period of diversification. Understanding the characteristics of the global tuna trade network and the changes in China's tuna trade pattern is important for China to optimize its tuna trade policies. In this study, the trade networks of fresh or chilled and elaborate tuna are researched using the complex network analysis method. Changes in the global tuna trade network pattern and the characteristics of China's tuna trade network are investigated. In addition, the Hubness measures index （HMI） is employed to measure the degree of China's reliance on its principal tuna export trading markets. Results are as follows: （1） From 2002 to 2020, the global fresh or chilled tuna trade network density increased from 0.027 to 0.030. The United States, Japan, and Spain had comparatively high betweenness centrality, whereas China's node degree, betweenness centrality, and strength were all small. （2） The network density of elaborate tuna trade increased from 0.049 to 0.063. Thailand's betweenness centrality ranked first in the world. China's node degree, betweenness centrality and strength all increased significantly, but its betweenness centrality ranked relatively low. （3） With the exception of a few countries （regions）, the HMI of China's primary tuna export markets are all low and generally declining. Thailand is China's greatest export market for tuna, but the average proportion of Chinese-origin tuna in Thailand's total tuna imports is only 6.9%. Conclusions are as follows: （1） The density of world tuna trade network is low with an overall rising trend. In trading network for fresh or chilled tuna, the United States, Japan and a few European countries maintain prominent positions and exert considerable influence over the industry. In the elaborate tuna trade network, Thailand is the most influential country; （2） The pattern of China's tuna trade network has changed significantly, and its status is progressively rising; however, its overall influence does not correspond to the ranking of trade scale; （3） China still has great potential for trade collaboration with major tuna import markets. [ABSTRACT FROM AUTHOR]

Road network studies attracted unprecedented and overwhelming interest in recent years due to the clear relationship between human existence and city evolution. Current studies cover many aspects of a road network, for example, road feature extraction from video/image data, road map generalisation, traffic simulation, optimisation of optimal route finding problems, and traffic state prediction. However, analysing road networks as a complex graph is a field to explore. This study presents comparative studies on the Porto, in Portugal, road network sections, mainly of Matosinhos, Paranhos, and Maia municipalities, regarding degree distributions, clustering coefficients, centrality measures, connected components, k-nearest neighbours, and shortest paths. Further insights into the networks took into account the community structures, page rank, and small-world analysis. The results show that the information exchange efficiency of Matosinhos is 0.8, which is 10 and 12.8% more significant than that of the Maia and Paranhos networks, respectively. Other findings stated are: (1) the studied road networks are very accessible and densely linked; (2) they are small-world in nature, with an average length of the shortest pathways between any two roads of 29.17 units, which as found in the scenario of the Maia road network; and (3) the most critical intersections of the studied network are 'Avenida da Boavista, 4100-119 Porto (latitude: 41.157944, longitude: − 8.629105)', and 'Autoestrada do Norte, Porto (latitude: 41.1687869, longitude: − 8.6400656)', based on the analysis of centrality measures. [ABSTRACT FROM AUTHOR]

Global liner shipping network (GLSN) constitutes an essential part of global maritime supply chains, but it could be vulnerable to disruptions. This paper develops an integrated framework for assessing the efficiency and vulnerability of GLSN. Specifically, a novel efficiency metric is proposed to quantify the performance of GLSN, and the framework enables modeling different levels of port disruption scenarios. Results show that the overall GLSN is quite robust under the partial disruption scenario of any single port (or of any single country's ports), but the damage to different countries is highly heterogeneous. Under dismantling scenarios where the identified most critical ports (countries) are successively disrupted, the GLSN vulnerability increases non‐linearly with an increasing level of disruption. Our findings demonstrate that it is necessary to monitor and protect the identified critical ports (countries); especially, avoiding their simultaneously complete disruptions is of vital importance to the robust functionality of GLSN. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this research was to find the acupoint combinations of manual and electro-acupuncture to treat chemotherapy-induced nausea and vomiting via the complex networks analysis. Methods: We conducted searches using PubMed, ScienceDirect, MEDLINE, Ovid, spring, Wiley, EMBASE, the Chinese biomedicine database, VIP information network, and China National Knowledge Infrastructure from the establishment of the databases to the August, 2023. Information about titles, journals, interventions, and main acupoints was extracted using the self-established “acupoint for prevention CINV data base” powered by EpiData. According to the level of literature evidence and sample size, the clinical trials and weights of the outcome indicators including nausea/vomiting efficiency were combined. After identifying articles, literature processing and complex network analysis were conducted. The degree distribution of each node, the probability distribution of node degree, the node clustering coefficient, and the distance matrix are calculated by software. Results: Of the 4001 screened publications, 489 were eligible after careful selection. Our result showed the acupoints ST36 and PC6 were the most common combination acupoints in both electro and manual acupuncture. In terms of efficiency, ST36, PC6, and CV12 are significantly effective acupoints for manual acupuncture, and the PC6 and ST36 are effective acupoint for electro-acupuncture. Conclusions: We found that the near-far collocation method has been commonly used for different types of acupuncture treatment in CINV. Zhongwan, Shangwan, and Liangmen have been mainly used as local acupoints, while Neiguan, Hegu, Quchi, Zusanli, Gongsun, TaiChong, and Neiguan have been mainly used as distal acupoints. From the effect analysis, acupuncture treatment of nausea manual acupuncture effect is better; acupuncture treatment of vomiting or electro-acupuncture effect is better. [ABSTRACT FROM AUTHOR]

Summary: This paper discusses the importance of feature extraction and structure similarity measurement in the analysis of complex networks. Social networks, biological systems, and transportation networks are just a few examples of the many phenomena that have been modeled using complex networks. However, analyzing these networks can be challenging due to their large size and complexity. Feature extraction techniques can help to simplify the network by identifying key nodes or substructures. Structure similarity measurement techniques can be used to compare different networks and identify similarities and differences between them. Previous research has suggested that real‐world complex networks are influenced by multiplex features and either local or global features. However, the interaction between these characteristics is not well understood. The proposed approach outperforms other graph similarity methods on publicly available datasets, with accurate estimations of overall complex network structures. Specifically, the approach based on cosine similarity outperforms as compared to existing methods. Overall, this study highlights the importance of considering various graph features–local and global features and their interactions in the analysis of complex networks. [ABSTRACT FROM AUTHOR]

The development of human society has led to the growing consumption of industrial products, which generates significant amounts of carbon emissions. However, relatively few in-depth studies have been conducted on the influence of different demand factors (e.g., household consumption, government consumption, export, and capital formation) on carbon emissions, which hinders the development of targeted industrial policies. To address this issue, an analytical framework based on input–output theory, the hypothesis extraction method, and complex network analysis was established to estimate the intrinsic influence of different demand factors on the embodied carbon transfer between provinces in China. The key findings can be summed up as follows: (1) The macro direction of China's embodied carbon transfer runs from resource-rich northern provinces to industrially developed southern provinces. (2) From the perspective of different demand factors, capital formation is the most significant contributor to China's embodied carbon transfer, with the construction industry being the most important driver. In contrast, government consumption causes the least embodied carbon transfer, but it has the highest average carbon emission intensity. (3) According to complex network theory, the carbon transfer networks via provinces and industries caused by exports are the most concentrated, with the manufacture of electrical machinery and electronic equipment serving as the main source of demand. In contrast, the carbon transfer network resulting from household consumption exhibits a high level of decentralization, with dominant sectors including electric power, gas and water production, and supply and other services. Based on these findings, this study is expected to contribute targeted suggestions with which provinces and industries can formulate demand-side carbon reduction policies for different demand factors, which will contribute to the achievement of "carbon peaking and carbon neutrality". [ABSTRACT FROM AUTHOR]

This study employs the 2012 to 2016 commuting flows of Florida to reexamine excess commuting (EC) through multiscale complex network analysis. The results reveal significant discrepancies in urban connectivity, hierarchy, and labor market geography between the actual (ACN) and optimal commuting networks (OCN). Compared to ACN, OCN illustrates an overly simplified and isolated polycentric commuting system. This is evident through reduced overall network connectivity, a restructured centrality distribution pattern, and fragmented community divisions. Consequently, these results underscore the importance of delving into the economic implications of commuting within the conventional EC framework. [ABSTRACT FROM AUTHOR]

Taking the marine industry as the breaktlirough point, based on tlie data of joint patent application of marine industry in tlie last 20 years, tliis study analyzes the cooperative network of different stages using complex network analysis. This approach reveals tlie evolution process, spatial structure, and influencing factors of network innovation perfbmiance of China's marine industry. The results reveal the following: (1) The cooperative network in 2000-2001 does not have tlie characteristics of complex networks. From 2002 to 2019, tlie "small world" and scale-free characteristics of the cooperation network gradually became obvious, indicating that China's marine industry had fbnned an organic connection of industry-university-research cooperation network since 2002. Before 2008, the network expansion speed was slow and tlie network structure gradually loosened. From 2009 to 2016, the network expansion speed accelerated and the network was optimized. After 2017, the network expanded rapidly and inter-agency cooperation was efficient. Taking 2008 and 2016 as dividing points, the network was divided into embryonic, exploration, and rapid development stages. The cooperative network has exliibited a loose, dual-core multilateral radiation structure, and has gradually evolved into a single-core topology witli China Offshore Oil Group Co., Ltd. (2) In the evolution process of cooperative innovation networks, universities have always been an important network resource transfer hub and have control of most resources. With the enhancement of the dominant position of innovation, state-owned enterprises' ability to control resources has greatly improved, gradually occupying the core position of the network. Under tlie influence of policy promotion, market selection, and practical needs for tlie transfbniiation and development of tlie marine industry, an industry-university-research cooperation network has been fbnned, with the enterprise-enterprise cooperation mode as tlie core and the enterprise-research cooperation mode and school-enterprise cooperation mode as tlie focus. (3) Under tlie impetus of coastal cities, the number of patents with which inland cities cooperate increased tlie fastest. The main body of industry-university-research innovation is concentrated in Beijing-Tiaiyin-Hebei, Yangtze River Delta, and Pearl River Delta. Beijing occupies tlie core position of the urban innovation network witli an absolute advantage. The urban cooperative network is characterized by an "umbrella" structure and has obvious "heavy tail" characteristics. Tliis shows tliat the development of marine science and teclmology mainly depends on the cooperation and innovation between coastal cities and a small number of inland cities, such as Beijing and Wuhan, affected by geographical distance and urban economic development level. (4) Impulse response analysis reveals that network scale, marine industry GDP, and scientific research personnel input can positively promote the innovation perfbnnance of cooperative networks. The research institutions, universities, and enterprises in the innovation subjects can stimulate the innovation output of the network and the importance of the tliree decreases in turn. Network density and small-world quotient are negatively correlated with network innovation output. [ABSTRACT FROM AUTHOR]

Objective: Based on proteomics and complex network analysis of normal rats after acupuncture serum exosomes （Acu-exo） on a variety of diseases onset mechanism and possible targets. Methods: Transmission electron microscopy, Western blot and nanoparticle tracking analysis were used to identify the serum exosomes extracted from normal rats 28 days after acupuncture. The proteins transported by Acu-exo were detected using Label Free quantitative proteomics technology, and their interacting proteins, molecular functions, participating signaling pathways and sources were searched using databases. The detected differential protein was used as the source node and the target node was used as the target node to construct the network topology. The effective targets of acupuncture were selected according to the insertion degree, weighted insertion degree and PageRank. Results: A total of 263 proteins were detected by Label Free quantitative proteomics. The highly correlated interacting proteins include antibody immunoglobulin G-1a, complement C3, fetoglobulin A, apolipoprotein A1, complement C2, etc. The signal pathways involved proteasome, complement, coagulation cascade and metabolic pathway. Conclusion: After acupuncture at Zusanli point in rats, the Acu-exo carry a large amount of active substances related to nerve, endocrine and immune regulation, which may be an important basis for acupuncture to exert network regulation. [ABSTRACT FROM AUTHOR]

Developmental dyslexia is characterized by a deficit of phonological awareness whose origin is related to atypical neural processing of speech streams. This can lead to differences in the neural networks that encode audio information for dyslexics. In this work, we investigate whether such differences exist using functional near-infrared spectroscopy (fNIRS) and complex network analysis. We have explored functional brain networks derived from low-level auditory processing of nonspeech stimuli related to speech units such as stress, syllables or phonemes of skilled and dyslexic seven-year-old readers. A complex network analysis was performed to examine the properties of functional brain networks and their temporal evolution. We characterized aspects of brain connectivity such as functional segregation, functional integration or small-worldness. These properties are used as features to extract differential patterns in controls and dyslexic subjects. The results corroborate the presence of discrepancies in the topological organizations of functional brain networks and their dynamics that differentiate between control and dyslexic subjects, reaching an Area Under ROC Curve (AUC) up to 0.89 in classification experiments. [ABSTRACT FROM AUTHOR]

Health-related quality of life (HRQOL) is the result of complex interactions between many different factors. But few studies to date have explored the structure of HRQOL. This study aimed to investigate the complex inter-relationship between HRQOL and influencing factors using network analysis. In all, 624 Chinese pregnant women in third trimester were recruited through recruiting sampling. We used regularized network analysis to create a complex network. The relationship with mother-in-law was the most central node followed by relationship with partner. We found the comorbidity of anxiety and depressive symptoms using network analysis. Physical- and mental-related quality of life were a "bridge node" connecting psychological factors with physiologic factors. The present network analysis highlights the strong link between relationship with mother-in-law or partner and HRQOL, which is also a unique phenomenon under Chinese culture. This analysis provides key variables for future intervention or improvement of pregnant women' HRQOL. [ABSTRACT FROM AUTHOR]

Link prediction is a key problem in the field of undirected graph, and it can be used in a variety of contexts, including information retrieval and market analysis. By "undirected graphs", we mean undirected complex networks in this study. The ability to predict new links in complex networks has a significant impact on society. Many complex systems can be modelled using networks. For example, links represent relationships (such as friendships, etc.) in social networks, whereas nodes represent users. Embedding methods, which produce the feature vector of each node in a graph and identify unknown links, are one of the newest approaches to link prediction. The Deep Walk algorithm is a common graph embedding approach that uses pure random walking to capture network structure. In this paper, we propose an efficient model for link prediction based on a hill climbing algorithm. It is used as a cost function. The lower the cost is, the higher the accuracy for link prediction between the source and destination node will be. Unlike other algorithms that predict links based on a single feature, it takes advantage of multiple features. The proposed method has been tested over nine publicly available datasets, and its performance has been evaluated by comparing it to other frequently used indexes. Our model outperforms all of these measures, as indicated by its higher prediction accuracy. [ABSTRACT FROM AUTHOR]

Objective To analyze the acupoint compatibility rules of acupuncture and moxibustion in the treatment of male infertility by using complex network technology, and to provide a basis for clinical acupuncture and moxibustion treatment and research on acupoint selection and prescription for male infertility. Methods The clinical literatures on acupuncture and moxibustion in the treatment of male infertility in the past 20 years were retrieved from the database of CNKI, Wanfang Data Knowledge Service Platform, VIP. com and PubMed, and relevant literatures were screened according to the inclusion and exclusion criteria, and a prescription database of acupuncture and moxibustion in the treatment of male infertility was established. The Apriori algorithm in SPSS Modelerl8. 0 was used for correlation analysis, the core acupoints nodes and compatibility relationship were analyzed, and the complex network co-occurrence map was drawn by Gephi0. 9. 2 software. Results A total of 91 literatures that met the standards were finally obtained, and 134 acupuncture prescriptions were extracted, involving 70 acupoints, and the total frequency of acupoints was 666 times. Among them, Guanyuan (RN4) was most frequently used (75 times, 11. 26%), followed by Shenshu (BI23), Sanyinjiao (SP6), Zusanli (ST36), Qihai (RN6), and Mingmen (GV4) points. The main acupoints returned to the Ren channel and the bladder channel of Foot Taiyang. Jiaohui acupoints were most frequently used for specific acupoints, followed by Wushu acupoints and Mu acupoints. Through complex network topology analysis, it was found that Guanyuan (RN4), Zusanli (ST36), Qihai (RN6), Shenshu (BI23), Sanyinjiao (SP6), Taichong (LR3) and other 23 acupoints were the core node points for acupuncture treatment of male infertility. Conclusion The acupoints selected in the acupuncture treatment for male infertility are mainly distributed in the lower abdomen, back and lower extremities, and Jiaohui acupoints, Wushu acupoints, and Mu acupoints are mostly selected for specific acupoints. [ABSTRACT FROM AUTHOR]

Identifying the most important nodes according to specific centrality indices is an important issue in network analysis. Node metrics based on the computation of functions of the adjacency matrix of a network were defined by Estrada and his collaborators in various papers. This paper describes a MATLAB toolbox for computing such centrality indices using efficient numerical algorithms based on the connection between the Lanczos method and Gauss-type quadrature rules. [ABSTRACT FROM AUTHOR]

The task of extracting and analyzing character networks from works of fiction, such as novels and movies, has been the object of a number of recent publications. However, only a very few of them focus on graphic novels, and even fewer on European graphic novels. In this paper, we focus on Thorgal, a bande dessinée, i.e. a comic of the French-Belgian tradition. We manually annotate all the volumes of this series, in order to constitute a corpus allowing us to extract its character network. We perform a descriptive analysis of the network structure and compare it to real-world and fictional social networks. We also study the effect of character filtering over the network structure. Finally, we leverage complex network analysis tools to answer two research questions from the literature, related to the similarity between Thorgal and the Saga of Icelanders; and to the position of women in the series. Our data and source code are both publicly available online. [ABSTRACT FROM AUTHOR]

• Proposed a novel approach combining complex network analysis and GNNs. • Reflected on the interactions between metabolic health and psychiatric symptoms. • Leveraged GNNs to explore schizophrenia symptoms and metabolic features' interplay. Schizophrenia is a severe mental disorder characterized by intricate and underexplored interactions between psychological symptoms and metabolic health, presenting challenges in understanding the disease mechanisms and designing effective treatment strategies. To delve deeply into the complex interactions between mental and metabolic health in patients with schizophrenia, this study constructed a psycho-metabolic interaction network and optimized the Graph Attention Network (GAT). This approach reveals complex data patterns that traditional statistical analyses fail to capture. The results show that weight management and medication management play a central role in the interplay between psychiatric disorders and metabolic health. Furthermore, additional analysis revealed significant correlations between the history of psychiatric symptoms and physical health indicators, as well as the key roles of biochemical markers(e.g., triglycerides and low-density lipoprotein cholesterol), which have not been sufficiently emphasized in previous studies. This highlights the importance of medication management approaches, weight management, psychological treatment, and biomarker monitoring in comprehensive treatment and underscores the significance of the biopsychosocial model. This study is the first to utilize a GNN to explore the interactions between schizophrenia symptoms and metabolic features, providing new insights into understanding psychiatric disorders and guiding the development of more comprehensive treatment strategies for schizophrenia. [ABSTRACT FROM AUTHOR]

Characterizing the effects of particle interaction and the influence of the fabric of granular materials is one of the primary challenges in studying the constitutive behavior of granular materials. The evolution of the fabric of granular materials and their response to applied stresses have been investigated extensively in the literature. Contact number is one of the most common metrics used to assess the evolution of the fabric of granular materials subjected to external loading. However, contact number is a limited metric as it incorporates only the effect of the particles in contact with a specific particle; it cannot be used to characterize the evolution of the fabric of granular materials at a mesoscale. A new metric that can incorporate the effect of particles in direct contact with a specific particle (as well as other particles within its vicinity) is much more powerful in characterizing the evolution of granular material fabric. Subgraph centrality (SC) is a complex network property that describes the change in the number of closed cycles in a network and represents a new metric for characterizing the contact network of the particles at the particle scale and mesoscale. 3D Synchrotron micro-computed tomography images (SMT) and SC were used to characterize the evolution of the fabric in five specimens, which were composed of two different types of silica sand particles subjected to axisymmetric triaxial loading. The effects of the specimens' initial density, confining pressure, kinematics of the particles, and particle morphology on the evolution of the contact network of the particles were investigated. The evolution of four node structures as one of the underlying fabric structures within the specimen was investigated to illustrate how the structure of the specimens was evolving and causing the change in the SC of the particles. Variation in the average SC of the specimens was correlated with their volumetric strain to demonstrate the relationship between the change in the contact structure of the particles and the constitutive behavior of sheared sand. [ABSTRACT FROM AUTHOR]

The present study deals with new research approaches to investigate social complexity in past societies, particularly in ancient Egypt. By using mathematical models such as Hidden Markov Models and Social Network Analysis, significant events in the history of Egypt are identified and their impact on state administration and its complexity is examined. The study demonstrates that the application of modern mathematical methods enables a new understanding of ancient Egypt and can also be relevant for the investigation of other past or present societies. It also highlights the connection between the growth of bureaucracy, nepotism, and climate change as factors contributing to the collapse of the Ancient Kingdoms in ancient Egypt. The study suggests that the application of mathematical analysis is a useful tool to comprehend the dynamics of complex societies and to explore contemporary trends and processes. [Extracted from the article]

Dominance Network (DN) analysis has been proposed to assess the efficiency of a set of operating units (OUs) that consume inputs to produce outputs. In a DN, the nodes represent OUs and the arcs correspond to dominance relationships (DR) between them. So far, this analysis has been applied in static scenarios where the OUs belong to the same time period. In this paper, the methodology is extended to the case in which the input and output data refer to multiple time periods tracking the performance evolution of each OU. The proposed Temporal Dominance Network (TDN) contains a node for each OU in each time period. TDN is a weighted directed acyclic graph. There exist contemporaneous DR (between nodes that correspond to the same time period) as well as non-contemporaneous DR (between nodes that correspond to different time periods). The union of these two types of DR defines the TDN. All these DR are transitive, which allows us to represent the TDN by its corresponding skeleton. Various filters and node layouts that can be used to help visualise the TDN are proposed. In addition, a number of network metrics are used to characterise the efficiency of the various OUs in the different periods as well as the corresponding productivity change between any two periods. [ABSTRACT FROM AUTHOR]

Taking the waterage passenger transportation lines in the Guangdong-Hong Kong-Macao Great Bay Area (GBA) as the data source to study its complex network characteristics can enrich the traffic geography research based on waterage passenger transportation and serve the construction of the GBA. The findings are as follows. Firstly, the waterage passenger transportation network (WPTN) in the GBA is basically sound with nodes flowing freely, but the network lacks in hierarchy as nodes with low accessibility occupy the majority and is overally dispersed with low-level modularization. The complex network can be divided into two communities, of which the nodes with the highest degree are China Ferry Terminal (CFT) in Hong Kong, Hong Kong International Airport (HKIA) and Shekou Port (SP) in Shenzhen, and the nodes most closely connected are HK Macao Ferry Terminal (HMFT) and Macao Outer Harbour Ferry Terminal (MOHFT). Secondly, the WPTN in the Great Bay Area has characteristics of some small world network and scale-free, of which the node distribution presents the "Matthew Effect". The average path length of the network is small, and the clustering coefficient is also small. CFT, HKIA and SP are the head nodes of the WPTN, which have the most growing feature and priority connection feature in the network. On the other side, it also shows that the internal differences of the complex network are large and the overall connectivity quality needs to be improved. Thirdly, SP is the most important node in the complex network, as its various indicators are all at the forefront. The positions of CFT, HKIA, Jiuzhou Port in Zhuhai and MOHFT in the complex network are also very important, and they have outstanding advantages in a single indicator respectively. Fourthly, from the perspective of spatial structure, the waterage passenger transportation capacity in the south of the GBA is more developed than that in the north, and that in the east is more developed than that in the west. And finally, the WPTN in the GBA has robustness and strong survivability, but the network development is not perfect enough with greater vulnerability, as the network connectivity depends too much on the core nodes and the stability is poor. After attacking the core nodes of the network, half of the remaining nodes will be greatly affected, and even some nodes become isolated ones. After calculation, there are only 31.34% of the complex network variables remained after event 3. In order to optimize the WPTN in the Great Bay Area, the suggestions are list below. First, for various core nodes, such as CFT, HKIA, SP and MOHFT, as they play important roles in the network, it is necessary to strengthen support to ensure their service capacity. Second, for edge nodes, such as Lianhuashan Port and Nansha Port in Guangzhou, Shunde Port and Gaoming Port in Foshan, we can make full use of the attraction and radiation of the city itself, fully investigate the market demand and develop new passenger transportation lines, especially increase the connections with non-core nodes and improve their position in the network, so as to enhance the stability and external attack tolerance of the whole network. And third, from the perspective of spatial pattern, it is necessary to focus on improving the waterage passenger transportation capacity in the north of the GBA and form a northern hub, in order to make the whole network more balanced. For future studies, we can focus on the evolution process and impact of the WPTN and the comprehensive research of waterage and land passenger transportation network as well. [ABSTRACT FROM AUTHOR]

Background: Recent development of bioinformatics tools for Next Generation Sequencing data has facilitated complex analyses and prompted large scale experimental designs for comparative genomics. When combined with the advances in network inference tools, this can lead to powerful methodologies for mining genomics data, allowing development of pipelines that stretch from sequence reads mapping to network inference. However, integrating various methods and tools available over different platforms requires a programmatic framework to fully exploit their analytic capabilities. Integrating multiple genomic analysis tools faces challenges from standardization of input and output formats, normalization of results for performing comparative analyses, to developing intuitive and easy to control scripts and interfaces for the genomic analysis pipeline. Results: We describe here NetSeekR, a network analysis R package that includes the capacity to analyze time series of RNA-Seq data, to perform correlation and regulatory network inferences and to use network analysis methods to summarize the results of a comparative genomics study. The software pipeline includes alignment of reads, differential gene expression analysis, correlation network analysis, regulatory network analysis, gene ontology enrichment analysis and network visualization of differentially expressed genes. The implementation provides support for multiple RNA-Seq read mapping methods and allows comparative analysis of the results obtained by different bioinformatics methods. Conclusion: Our methodology increases the level of integration of genomics data analysis tools to network inference, facilitating hypothesis building, functional analysis and genomics discovery from large scale NGS data. When combined with network analysis and simulation tools, the pipeline allows for developing systems biology methods using large scale genomics data. [ABSTRACT FROM AUTHOR]

Batsmen are accorded a certain precedence for better batting ability over their peers. The batsmen in cricket are assessed mostly based on their batting average. However, comparing players by batting average over different timelines does not yield the appropriate results, as the batting productivity of a particular player varies in unique ways across the different formats of the game. Using batting averages for comparison does not include factors such as the speed of scoring runs and the frequency of milestones achieved. The objective of this study is to present an effective knowledge-based mechanism for judging and comparing the batting strength of players in different formats of cricket. This methodology uses a complex network consisting of effective features that are subsequently integrated to formulate a Batting Precedence Score, which is further incorporated into an efficient Batting Precedence Score algorithm. In addition, we created a structured World Wide Batsman Dataset (WWBD) for our analysis based on the ESPN Cricinfo dataset. The results of extensive experiments demonstrate that the proposed method provides promising insights. The batting precedence of the proposed method is further compared with those of existing methods, and the proposed method displays better results. [ABSTRACT FROM AUTHOR]

Dopaminergic neurons of the substantia nigra pars compacta (SNC) and ventral tegmental area (VTA) exhibit spontaneous firing activity. The dopaminergic neurons in these regions have been shown to exhibit differential sensitivity to neuronal loss and psychostimulants targeting dopamine transporter. However, it remains unclear whether these regional differences scale beyond individual neuronal activity to regional neuronal networks. Here, we used live-cell calcium imaging to show that network connectivity greatly differs between SNC and VTA regions with higher incidence of hub-like neurons in the VTA. Specifically, the frequency of hub-like neurons was significantly lower in SNC than in the adjacent VTA, consistent with the interpretation of a lower network resilience to SNC neuronal loss. We tested this hypothesis, in DAT-cre/loxP-GCaMP6f mice of either sex, when activity of an individual dopaminergic neuron is suppressed, through whole-cell patch clamp electrophysiology, in either SNC or VTA networks. Neuronal loss in the SNC increased network clustering, whereas the larger number of hub-neurons in the VTA overcompensated by decreasing network clustering in the VTA. We further show that network properties are regulatable via a dopamine transporter but not a D2 receptor dependent mechanism. Our results demonstrate novel regulatory mechanisms of functional network topology in dopaminergic brain regions. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic, which began as an international health crisis, has quickly become an economic crisis on both national and global scale. As economies are linked through global value chains, this crisis has significantly affected the global economy. The strict measures to prevent the rapid spread of the virus negatively affected the supply and demand relations. In this study, the authors examine the inter-provincial supply–demand relations in Turkey via a complex network analysis. The analysis has the following two stages: examination of topological properties and simulation estimates. The topological analysis reveals the complexity and core–periphery structure of the inter-provincial trade network. Istanbul comes to the forefront as the strongest node in the network in terms of both supply and demand. Simulation results reveal that such economic dependence on Istanbul alone makes the inter-provincial trade system more vulnerable against supply or demand shock. [ABSTRACT FROM AUTHOR]

Because consideration of segments and valves is essential for evaluating the reliability and resilience of water distribution networks (WDNs) when shutdowns are required, a quick method of identifying critical and vulnerable segments and valves would benefit utilities. While the importance and vulnerability of segments can best be evaluated by extensive hydraulic analysis, hydraulic analyses can be time consuming. It can also be challenging to visualize the segments of a water distribution network and their associated valves. To address these limitations, this study develops a method based on graph theory to identify important and vulnerable segments without hydraulic calculations. The method generates a matrix that represents how reachable water sources are from segments when a given segment must be isolated while distinguishing between continuous water sources and ephemeral storage. This study also applies measures from graph theory to determine the number of valves to operate to isolate a segment and provides a rigorous proof to support the intuitive equation. A method to visualize the connectivity of segments with the graph-theory measures is demonstrated. The developed methods are applied to multiple valving scenarios of a case study and two real WDNs. Correlations between graph-theory based measures derived from the segment-valve topology and hydraulic simulation-based criticality are higher than in previous studies that apply graph theory to the pipe-junction topology of WDNs (r≥0.6). Results indicate that the developed methods can be used by utilities as a preliminary screening to eliminate the need for some hydraulic simulations. These findings are expected to provide decision support for utilities. [ABSTRACT FROM AUTHOR]

Obtaining an efficient prediction of the Pacific Decadal Oscillation (PDO) phase transition is a worldwide challenge. Here, we employed the climate network analysis to uncover early warning signals prior to a PDO phase transition. This way an examination of cooperative behavior in the PDO region revealed an enhanced signal that propagated from the western Pacific to the northwest coast of North America. The detection of this signal corresponds very well to the time when the upper ocean heat content in the off‐equatorial northwestern tropical Pacific reaches a threshold, in which case a PDO phase transition may be expected with the arising of the next El Ni n∼o/La Nin∼ a event. The objectively detected early warning signal successfully forewarned all the six PDO phase transitions from the 1890–2000, and also underpinned the possible PDO phase transition around 2015, which may be triggered by the strong El Nin∼o event in 2015–2016. Plain Language Summary: The Pacific Decadal Oscillation (PDO), as one of the main internal decadal climate variabilities, has been shown to have substantial impacts on both the climate system and other parts of Earth system. Due to its complexity, however, it is still challenging to predict its phase changes. Here, a new technique from climate network analysis was employed to capture the potential precursors of the phase transition. By calculating the linkages between the warm and cold regions of the PDO spatial pattern, a sudden strengthening of negative connections between the two regions was detected a few years before the phase transition. This early warning signal arises from the cooperative behavior of the considered regions, that is, many more grid points from the two regions are becoming negatively linked at the time of alarming. This time corresponds well to the moment when the upper ocean heat content in the off‐equatorial northwestern tropical Pacific reaches a threshold, in which case a PDO phase transition may be expected with the arising of the next El Nin∼o/La Nin∼a event. Since the early warning signal is objectively detected from the climate network analysis, it may be used in operations for improved predictions of the PDO. Key Points: An early warning signal for the Pacific Decadal Oscillation (PDO) phase transition is detectedThe signal arises mainly from the cooperative behaviors of the grid points in the PDO regionA new strategy for the prediction of the PDO phase transition is proposed [ABSTRACT FROM AUTHOR]

Enhancing the performance of water distribution networks (WDNs) on a day-to-day basis or under extreme disturbances is an utmost priority for utilities. Previous research has characterized the structure of WDNs in the pipe–junction or segment–valve representation to gain insight on various aspects of their performance; however, the research on characterizing WDN structure in a hierarchical representation and its relationship with performance is lacking. Two key physical properties of WDNs are loops and pipe diameters that are organized in a hierarchical way. Novel indicators have been created to quantify the network hierarchy related to these key properties in other spatial flow distribution networks: loop nestedness and pipe diameter gradation along flow paths. The goal of this study is to adopt such indicators to characterize the hierarchy of WDNs and evaluate its relationship with WDN performance. This study applies a hierarchical decomposition process to model the relationships among loops as a tree network for quantifying loop nestedness. Flow paths of monotonically increasing and decreasing pipe diameters are traced to quantify pipe diameter gradation. Statistical distributions are approximated for these two indicators. Then, relationships between these network hierarchy indicators and two performance indicators (measuring path redundancy and power surplus) are identified. For 15 benchmark networks, this study finds the statistical distributions representing loop nestedness and pipe diameter gradation closely follow a power law. Results suggest that gradual pipe diameter gradation along flow paths and high loop nestedness increase WDN path redundancy, and gradual pipe diameter gradation increases WDN power surplus. The study demonstrates that the hierarchical analysis of WDNs can significantly supplement traditional topological analyses in explaining WDN performance. [ABSTRACT FROM AUTHOR]

Economic activities among multiple regions are always accompanied by carbon transfers. Analyzing coupling characteristics of economic activities and carbon transfer linkages based on the supply-demand relationships, can further reveal the networked structures of the multiregional interactions and common development trend of various industries, shedding light on carbon emission governance and high-quality development. This study advances novel coupling network models at the regional and industrial levels, and empirically analyzes the coupling characteristics in China based on the input-output data in 2012, 2015, and 2017. The findings reveal a noticeable decoupling process of economic activities and carbon transfers, but with distinct characteristics at the regional and industrial levels. The widening differences in coupling among provinces indicate increasing regional disparities. The decoupling process at the industrial level is primarily driven by the decreased connectivity in networked carbon transfers, instead of economic activities, reflecting the significant variations of industries' low-carbon development. The carbon decoupling process is notably more pronounced in supply-demand chains associated with export as the final use, compared to those linked with capital formation and final consumption. Analysis of coupling characteristics and the identification of decoupling evolution process enhance our understanding of the relationship between economic activities and carbon transfer, and may provide valuable insights for prioritizing actions and achieving efficient carbon emission reduction. • The linkages of economic and carbon transfer in China decouples over time. • The analysis integrates input-output analysis with complex network model. • Different network models are applied at regional level and industrial level. • Multi-level network analysis on coupling characteristics. • The results suggest action priorities to achieve sustainable developments. [ABSTRACT FROM AUTHOR]

• Investigate structural robustness of global liner shipping network (GLSN) by motif analysis. • Propose a motif-based node centrality metric and two motif-based link-adding strategies. • The proposed node centrality metric is effective in identifying critical ports for GLSN structural robustness. • The proposed link-adding strategies can effectively improve the GLSN structural robustness. Liner shipping carries about 70% of the total value of world's seaborne trade, and thus the robust functioning of global liner shipping network (GLSN) is essential for building robust global maritime logistics systems. However, understanding and improving the underlying structural robustness of GLSN is an important challenging topic. This paper contributes to this topic by proposing a motif-based network science approach to address two crucial tasks therein. First, based on the extensive mining of four-node motifs in the GLSN, we propose a novel node centrality metric named motif centrality to characterize the importance of a port in the GLSN, which proves its effectiveness in identifying critical ports for maintaining the GLSN structural robustness. Second, after assessing the GLSN structural robustness against some worst-case scenarios of port disruptions, we propose two motif-based link-adding strategies to improve the GLSN structural robustness. Experimental results prove their effectiveness as compared with random link-adding strategies. For further scrutinization, one strategy is extended to the individual country level and is found useful in improving the structural robustness of liner shipping connections of most countries, especially many underdeveloped maritime countries. Some practical and theoretical implications are finally discussed. [ABSTRACT FROM AUTHOR]

Nitrous oxide (N 2 O) is the third most potent greenhouse gas (GHG) and the most important ozone depleting substance. But how global N 2 O emissions are connected through the interwoven trade network remains unclear. This paper attempts to specifically trace anthropogenic N 2 O emissions via global trade networks using a multi-regional input-output model and a complex network model. Nearly one quarter of global N 2 O emissions can be linked to products traded internationally in 2014. The top 20 economies contribute to about 70% of the total embodied N 2 O emission flows. In terms of the trade embodied emissions classified by sources, cropland-, livestock-, chemistry-, and other industries-related embodied N 2 O emissions account for 41.9%, 31.2%, 19.9%, and 7.0%, respectively. Clustering structure of the global N 2 O flow network is revealed by the regional integration of 5 trading communities. Hub economies such as mainland China and the USA are collectors and distributors, and some emerging countries, such as Mexico, Brazil, India, and Russia, also exhibit dominance in different kinds of networks. This study selects the cattle sector to further verify that low production-side emission intensities and trade cooperation can lead to N 2 O emission reduction. In view of the impact of trade networks on global N 2 O emissions, achieving N 2 O emission reduction calls for vigorous international cooperation. • Nearly one quarter of global N 2 O emissions can be linked to international trade. • Trade-related virtual N 2 O emission transfers shape a highly heterogeneous network. • Achieving N 2 O emission reduction calls for vigorous international cooperation. [ABSTRACT FROM AUTHOR]

• Ecological sources were selected based on GEP. • A Resistance-Plane framework considering karst characteristics was proposed. • Identifying key areas with real geography to optimize ecological networks. • Combining complex network analysis to assess EN connectivity and stability. 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. [ABSTRACT FROM AUTHOR]

With the widespread application of complex network theory, identifying vital nodes is an important part of complex network analysis, which has been a key issue in analyzing the characteristics of network structure and functions. Although many centrality measures have been proposed to identify vital nodes, there is still a lack of an objective performance testing platform for evaluating vital nodes identification algorithms. This study introduces the complex scheduling network as an objective performance testing platform where the identification of vital nodes directly determines the decision-making in each decision scenario through the development of heuristic algorithms upon centrality measures, and therefore, the optimization goals of scheduling problems can be regarded as an objective index for evaluating vital node identification algorithms. Finally, the undirected network obtained by the open shop scheduling problem is taken as an example to analyze and evaluate the performance of various vital node identification algorithms. [ABSTRACT FROM AUTHOR]

A graph G = (V , E) is δ -hyperbolic if for any four vertices u , v , w , x , the two larger of the three distance sums d (u , v) + d (w , x) , d (u , w) + d (v , x) , d (u , x) + d (v , w) differ by at most 2 δ ≥ 0. This paper describes the eccentricity terrain of a δ -hyperbolic graph. The eccentricity function e G (v) = max ⁡ { d (v , u) : u ∈ V } partitions vertices of G into eccentricity layers C k (G) = { v ∈ V : e G (v) = r a d (G) + k } , k ∈ N , where r a d (G) = min ⁡ { e G (v) : v ∈ V } is the radius of G. The paper studies the eccentricity layers of vertices along shortest paths, identifying such terrain features as hills, plains, valleys, terraces, and plateaus. It introduces the notion of β -pseudoconvexity, which implies Gromov's ϵ -quasiconvexity, and illustrates the abundance of pseudoconvex sets in δ -hyperbolic graphs. It shows that all sets C ≤ k (G) = { v ∈ V : e G (v) ≤ r a d (G) + k } , k ∈ N , are (2 δ − 1) -pseudoconvex. Several bounds on the eccentricity of a vertex are obtained which yield a few approaches to efficiently approximating all eccentricities. [ABSTRACT FROM AUTHOR]

Software systems are usually designed in a modular and hierarchical fashion, where functional responsibility of a system is decomposed into multiple functional software elements optimally such as subsystems, modules, packages, classes, methods, and functions. These elements are coupled with each other with some kind of dependency relationships to some degree, and their interactions naturally form a graph or network structure. In this study, we generated the static function-call graphs of several open-source software systems, where functions were the most basic type of interacting elements calling each other. Then, we analyzed the call graphs both visually and topologically using the techniques of complex network analysis. We found the call graphs to reveal scale-free and small-world network properties similar to the findings of the previous studies. In addition, we identified the most central and important functions in each call-graph using several centrality measures. We also performed community analysis and found that the call graphs exhibited a tendency to form communities. Finally, we showed that analysis of static function-call graphs of software systems through complex network analysis has the potential to reveal useful information about them. [ABSTRACT FROM AUTHOR]

In this work, we propose a novel centrality metric, referred to as star centrality, which incorporates information from the closed neighborhood of a node, rather than solely from the node itself, when calculating its topological importance. More specifically, we focus on degree centrality and show that in the complex protein–protein interaction networks, it is a naive metric that can lead to misclassifying protein importance. For our extension of degree centrality when considering stars, we derive its computational complexity, provide a mathematical formulation, and propose two approximation algorithms that are shown to be efficient in practice. We portray the success of this new metric in protein–protein interaction networks when predicting protein essentiality in several organisms, including the well-studied Saccharomyces cerevisiae, Helicobacter pylori, and Caenorhabditis elegans, where star centrality is shown to significantly outperform other nodal centrality metrics at detecting essential proteins. We also analyze the average and worst-case performance of the two approximation algorithms in practice and show that they are viable options for computing star centrality in very large-scale protein–protein interaction networks, such as the human proteome, where exact methodologies are bound to be time and memory intensive. [ABSTRACT FROM AUTHOR]

Abstract Mercury emission flows in the global supply chains have evolved into an ever-increasing complex network. However, the underlying structural features remain unknown. Therefore, the global embodied mercury flow network was constructed to reveal the characteristics of energy-induced mercury emissions embodied in international trade at both national and sectoral scales. The small-world nature of the global mercury flows network was identified at both scales. Results showed that the global mercury flow network can be divided into 4 national communities, within which the spillover effects of the interventions in one region spread more easily. Detecting the mercury-intensive supply-chain clusters highlights the importance of monitoring these clusters that dominate mercury emissions in global supply chains, which could offer insights on where policy can be implemented effectively. Moreover, vital regions (e.g., mainland China, the USA, and Germany) and sectors (e.g., Petroleum, Chemical and Non-Metallic Mineral Products , Metal Products and Electrical and Machinery in mainland China) for global mercury control have been unveiled by using an integrated centrality measurement system. Our results highlight that, for the overall mercury reduction, regional and even global collaboration should be enhanced along with efforts in individual regions, and enterprises in these important sectors should invest more to green their cluster-wise supply chains. Graphical abstract Unlabelled Image Highlights • Understanding the global mercury flow network is important for policymakers. • Interactions between mercury pollution and economic activities were demonstrated. • A method combining MRIO and complex network analysis was used. • The mercury flow network was divided into 4 national and 106 sectoral clusters. • Critical economies and sectors were identified by a set of centrality indicators. [ABSTRACT FROM AUTHOR]

Objective: Atrial fibrillation (AF) is the most common type of persistent arrhythmia. Early diagnosis and intervention of AF is essential to avert the further fatality. The technique of noninvasive electrical mapping, especially the body surface potential mapping (BSPM), has a more practical application in the study of predicting AF, when compared with the invasive electrical mapping methods such as the epicardial mapping and interventional catheter mapping. However, the prediction of AF with noninvasive signals has been inadequately studied. Thus, the aim of this paper was to analyze the properties of atrial dynamic system based on the noninvasive BSPM signals (BSPMs), using the recurrence complex network, and consequently to evaluate its role in predicting the recurrence of AF in clinical aspect.Method: Twelve patients with persistent AF were included in this study. Their preoperative and postoperative BSPMs were recorded. Initially, the preoperative BSPMs were transformed into the recurrence complex network to characterize the complexity property of the atria. Subsequently, the parameters of recurrence ratio (REC), determinism (DET), entropy of the diagonal structure distribution (ENTR), and laminarity (LAM) were calculated. Furthermore, the difference in the parameters in the four regions of the body and the difference obtained from the dominant frequency (DF) method were compared. Finally, the results obtained for the atrial dynamic system complexity from a 12-lead electrocardiogram (ECG) from the BSPMs were discussed.Results: Our study revealed that the patients whose REC is greater than an average threshold, and with a lower LAM presented a much higher possibility of AF recurrence, after the AF surgery.Conclusions: The recurrence complex network is a useful and convenient way to evaluate the nonlinear properties of the BSPMs in patients with AF. It has good immunity to the lead position and has a potential role in the understanding of predicting the recurrence of AF. [ABSTRACT FROM AUTHOR]

Topological analyses are crucial when assessing network robustness or generating synthetic power grids. In the case of synthetic power grids, topological considerations may be included during the generation process or they may be used as validation criteria once synthetic networks are generated. These synthetic grids can be used as case studies only if their topology is statistically consistent with real power networks. With a view to looking into power-network topology, we analyze the topology of fifteen European transmission networks by using complex-network metrics. The study includes two voltage levels: 400 kV and 200 kV. We study these levels both independently and as a single combined grid. Degree distribution, characteristic path length, network diameter, betweenness centrality and global clustering coefficient are explored in order to understand network topology and to explain observed differences among countries. We analyze empirically whether those metrics scale or not with network size as well as the characterization of power grids as small-world networks. Our conclusions improve the current understanding of power network topology, which is essential for generating synthetic power grids and in the assessment of network robustness. [ABSTRACT FROM AUTHOR]

According to an urban planning directive issued by the Chinese Central Government in early 2016, the Community Opening Policy will be put on the agenda to ameliorate transportation networks, which has aroused much attention regarding the coming changes in traffic, especially in road network structures. Nevertheless, few studies were concerned with the direct and indirect impacts of opening gated communities. This study aims to analyze the impact of opening gated communities on road structure by integrating ant colony optimization (ACO) and complex graph theory and to investigate its influence on resident commuting efficiency and the traffic flow centroid. First, with a traffic road network based on OpenStreetMap (OSM), alteration in the connectivity between two road nodes and the resident overall commuting time and distance are computed using an ACO algorithm. Second, using complex graph theory, the attraction index of every road is calculated, and the congestion area before and after the opening policy is obtained. The research areas include Beijing, Shanghai, Guangzhou, and Shenzhen, which are first-tier Chinese cities. The results demonstrate that opening gated communities will increase the connectivity and accessibility of the current road network by 9.43–29.80%, generating a decrease of 2.57–4.50% in resident commuting time for short-distance travel. Furthermore, with the decline in traffic stream in the urban trunk roads, the number of potentially congested junctions will decrease, resulting in an alteration in the spatial position and coverage area of the urban congestion areas. This study carried out quantitative research into the effect of opening gated communities, providing a reference for urban planners and government. [ABSTRACT FROM AUTHOR]

Global phosphorus (P) trade, whose resilience is crucial regarding food security globally, involves numerous risks. By combining complex network analysis (CNA) with information-based ecological network analysis (IENA), this study introduces an integrative method for analyzing the evolution of phosphorus trade networks (PTNs) globally from a production perspective, while focusing on the 1990–2020 period; it also provides a conceptual framework and indicators to assess PTN resilience based on that information. Results show that PTN resilience averaged 0.24 and fluctuated over time while being significantly influenced by phosphate fertilizer (PF) trade networks. PTNs exhibited an average redundancy of 4.54 and an average efficiency of 0.55. Various trade paths enabled countries in PTNs to maintain stable sources of import or export. PF had a more diversified trade path and a more balanced connectivity structure than those of phosphate rock (PR). A fluctuating PR share in the core countries' trade resulted in resilience fluctuations; however, path diversity reduced the impact of shocks emanating from the core countries. Overall, this study highlights that global P management could be improved through more stable national policies of the core countries, improved international cooperation frameworks, and information transparency. [Display omitted] • This paper examines the evolution of phosphorus trade network from 1990 to 2020. • We quantified the resilience by combining information-based ecological network analysis and complex network analysis. • Diversified trade path enabled countries to maintain stable sources of import or export. • Phosphate fertilizer trade had a more balanced network structure compared to those of phosphate rock trade. • Change of phosphate rock share in the core countries' trade resulted in resilience fluctuations. [ABSTRACT FROM AUTHOR]

Increasing globalization intensifies land redistribution via global supply chains. Interregional trade not only transfers embodied land but also displaces the negative environmental impact of land degradation from one region to another. This study sheds light on land degradation transfer by focusing on salinization directly whereas previous studies have extensively assessed the land resource embodied in trade. To analyze the relationships among economies under interwoven embodied flows, this study integrates complex network analysis and input-output method to observe the endogenous structure of the transfer system. By focusing on irrigated land with higher crop yields than dryland farming, we make policy recommendations on food safety and proper irrigation. The results of the quantitative analysis show that the total amount of saline and sodic irrigated land embodied in global final demand are 260978.23 and 424291.05 square kilometers respectively. Salt-affected area of irrigated land is imported by not only developed countries but also large developing countries such as Mainland China and India. Exports of embodied salt-affected land in Pakistan, Afghanistan, and Turkmenistan are pressing issues, accounting for nearly 60% of total exports from net exporters worldwide. It is also demonstrated that embodied transfer network has a basic community structure of three groups due to regional preference in agricultural products trade. • Transfer network of land degradation within global supply chains is tracked directly. • Salinization on irrigated land is investigated from saline and sodic two aspects. • Three main communities are detected to understand the regionalization of embodied salinization. • Large developing countries also transfer pressure of land degradation to others. [ABSTRACT FROM AUTHOR]

Network backbone extraction techniques reduce the size of networks while trying to preserve their key topological and spatial features. Various backbone extraction algorithms have been proposed in different scientific fields. Although of clear interest to transport geographers, backbone extraction techniques have been adopted unevenly and in an ad hoc fashion in transport geography research. In this paper we therefore present a conceptual and experimental comparison of backbone extraction techniques in a transport-geographical context, and explore the new insights each technique can offer to enhance our understanding of the Southeast Asian intercity air transport network (SAAN). We review six frequently-used methods, i.e. global weight thresholding method (GWTM), k-core decomposition method (KCDM), minimum spanning tree method (MSTM), primary linkage analysis method (PLAM), multiple linkage analysis method (MLAM), and the disparity filter algorithm method (DFAM), and elaborate their analytical essence by applying them to extract the backbone of the SAAN. The abstracted networks are compared in terms of their geographical and topological structures using the initial network as a benchmark. This comparison is then used to point out the different techniques' potential in light of different transport geography research applications. [ABSTRACT FROM AUTHOR]

Transport sector’s substantial contribution to global greenhouse gas emissions has made it a growing area of study and concern. In order to identify trends and characteristics of carbon emissions research in the transportation sector we conducted a Bibexcel and complex network analysis for the period 1997–2016. In addition, we identify critical themes and contributions of research articles using h-index , PageRank and cluster analysis. We report contribution of countries, authors, institutions and journals, as well as performance of citations and keywords. Co-citing situations between different countries, authors, and institutions are also analyzed using network analysis. Between 1997 and 2016 we found a rise in publications on carbon emissions in the transportation sector and increased cooperation between countries, authors, and institutions. Authors from the USA, China and United Kingdom published the most articles and articles with the highest academic influence. Tsinghua University from China is the leading institution in carbon emissions research in the transportation sector. The most widely published author and cited author is Dr. He. We conclude our analysis by analyzing keywords and trends to suggest critical topic areas of future research. The systematic approach undertaken in this study can be extended and applied to other research topics and fields. [ABSTRACT FROM AUTHOR]

The predicted link expiration time (LET)-based approach is currently the only promising approach to determine stable data gathering (DG) trees for mobile sensor networks, and the use of this approach requires the sensor nodes to be location and mobility aware (which is energydraining on the nodes). The objective of this paper is to investigate the use of location and mobility-independent graph theoretic metrics (such as Neighborhood Overlap: NOVER, Bipartivity Index: BPI and Algebraic Connectivity: ALGC) that could be locally computed by each sensor node on the egocentric network of an edge to quantify the stability of the links. The egocentric network of an edge comprises of the end nodes of the edge and their neighbors (as vertices) and links incident on the end nodes of the edge (as edges). We hypothesize that an edge whose egocentric network has a larger NOVER or a smaller BPI or a larger ALGC score should have its end nodes share a significant fraction of their neighbors and be a short distance link that is relatively more stable. Simulation results indicate that the DG trees determined based on the graph theoretic metrics are significantly more stable and energy-efficient compared to that of the LET-based DG trees. [ABSTRACT FROM AUTHOR]

This article studies the topological consistency of spatial networks due to node aggregation, examining the changes captured between different network representations that result from nodes’ grouping and they refer to the same socioeconomic system. The main purpose of this study is to evaluate what kind of topological information remains unalterable due to node aggregation and, further, to develop a framework for linking the data of an empirical network with data of its socioeconomic environment, when the latter are available for hierarchically higher levels of aggregation, in an effort to promote the interdisciplinary research in the field of complex network analysis. The research question is empirically tested on topological and socioeconomic data extracted from the Greek Maritime Network (GMN) that is modeled as a non-directed multilayer (bilayer) graph consisting of a port-layer, where nodes represent ports, and a prefecture-layer, where nodes represent coastal and insular prefectural groups of ports. The analysis highlights that the connectivity (degree) of the GMN is the most consistent aspect of this multilayer network, which preserves both the topological and the socioeconomic information through node aggregation. In terms of spatial analysis and regional science, such effects illustrate the effectiveness of the prefectural administrative division for the functionality of the Greek maritime transportation system. Overall, this approach proposes a methodological framework that can enjoy further applications about the grouping effects induced on the network topology, providing physical, technical, socioeconomic, strategic or political insights. [ABSTRACT FROM AUTHOR]

Molecular dynamics simulations have been performed to investigate the structural properties of the extended hydrogen-bond (H-bond) network of liquid formamide(FA)-water(W) mixtures over the whole concentration range. Instruments of complex network analysis have been utilized to identify and quantify the structural aspects of the H-bond networks involving FA-FA, W-W, and FA-W H-bonding interactions. These include the degree distributions, the distributions of network neighborhood numbers up to the second order, and the geodesic distributions. Collectively, they provide information regarding not only the local H-bonding environment of the species but also the H-bonding environment beyond their immediate neighbors. These analyses revealed that although the connectivity patterns within the H-bond network of the mixtures are altered significantly with the composition, the degree of connectivity of the networks are well maintained. Neighborhood analysis revealed that in the water rich solution nearly all FA molecules form the H-bonded clusters of the form FA ⋯ W ⋯ W and that the H-bonded cluster [ W ⋯ W ⋯ FA ⋯ FA ⋯ W ⋯ W  ] is the dominant structural motif involving FA dimers. According to the geodesic analysis, population of the chain-like H-bond pathways composed of one type of molecular species increases with the increasing mole fraction of the other type of species, until the mixture reaches the equimolar composition. [ABSTRACT FROM AUTHOR]