Complex Network Method Research Articles

The evolution and influencing factors of international tungsten competition from the industrial chain perspective

The concentrated supply of tungsten and its important role in strategic emerging industries have led to intense competition between importers. Based on trade data from 2000 to 2018, this study analyses the evolution and influencing factors of international tungsten competition from the industrial chain perspective using a complex network method and an extended gravity model. First, the results reveal that competition varies at different stages. Upstream competition is unstable; the import market for upstream and midstream products is expanding; downstream competition is becoming increasingly intense, and importers are gradually becoming integrated. Second, global tungsten competition is getting tighter, with the fiercest competition between Asia and Europe. In addition, the US, Japan and Germany has remarkable competitiveness in mid-downstream stages of the industrial chain. Finally, an analysis of the influencing factors shows that consumption level drives the competition for upstream and midstream products, while technological progress and Sino-US trade frictions impede the competition of mid-downstream products. Besides, rising political risk in exporting countries intensifies competition throughout the tungsten industry. Corresponding policy recommendations are proposed to promote a stable and orderly trade competition environment in the tungsten industry.

Garment recommendation in an e-shopping environment by using a Markov Chain and Complex Network integrated method

In the e-commerce environment, website-based recommendation systems have been developed in order to help consumers without professional fashion-related knowledge to identify the most relevant garment products satisfying their specific personalized requirements in terms of fashion and functionalities. However, there are two main drawbacks in the existing recommendation systems: (1) the existing shopping data and/or human body data-based recommendations cannot effectively deal with fashion trends; (2) the shoppers’ experience and knowledge are rarely considered in these systems. To solve these two drawbacks, we propose in this paper a new garment recommendation mechanism using a Markov Chain and Complex Network integrated method. The Markov Chain method is employed as a suitable tool permitting one to extract fashion trends from a lower quantity of shopping data. The Complex Network method is used to formalize uncertain relations between human body shapes, garment fitting effects and garment features, provided by shopping experts. Compared with other existing recommendation methods, the proposed method is validated to be more robust and more interpretable owing to its capacity of handling body types and fashion trends.

EXAMINING NETWORK STRUCTURES AND DYNAMICS OF WORLD ENERGY COMPANIES IN STOCK MARKETS: A COMPLEX NETWORK APPROACH

The energy sector occupies a mainstay role in overall growth in the modern worldwide economy. Therefore, it is essential to examine network structures and dynamics of leading energy companies of the world through complex network methods. Because, complex network methods are significant tools of studying the static and dynamics properties of the stock market, which allows us to better comprehend the stock market. We use daily prices of 147 energy stocks belonging to 34 countries of the world from 2006-2019. In addition to the overall sample, we explore networks for two sub-periods to examine the topological evolution during global recession of 2008, and energy and European debt crisis of 2011. Our results show substantial clustering of energy companies based on their geographic position during overall sample period. However, the crisis periods lead to a break in Asian and European clusters and only one prominent cluster appears in all the periods belonging to North American energy companies. We also observe few top US and European based companies occupying important and great global influence positions in the networks. In addition, time-varying topological measures indicate contraction of networks during crisis time, and an expansion in the recovery periods. More implications are also discussed.Keywords: energy companies; complex network; threshold network; minimum spanning tree; stock market; crisisJEL Classifications: C18; E32; E44; G01; G14; G15; G19DOI: https://doi.org/10.32479/ijeep.11287

Signless-laplacian eigenvector centrality: A novel vital nodes identification method for complex networks

• An iterative centrality updated framework between vertices and edges is proposed. • This iterative framework is convergent. • The convergence values of vertices are defined as the new vertex centralities. • The new centrality scores correspond to the principal eigenvalues of the signless-Laplacian matrix. • When applying on many data sets from various fields, all obtain reliable and even better results. Identifying important and influential nodes in complex networks is crucial in understanding, controlling, accelerating or terminating spreading processes for information, diseases, innovations, behaviors, and so on. Many existing centrality methods evaluate a node’s importance (or centrality) according to its neighbors, but the effects of its incident edges are always ignored or treated equally. However, in reality, edges always play different roles, which are usually measured by the edge centrality. Note that the centrality of a vertex is affected by the centralities of its incident edges, and conversely the centrality of an edge is determined by the centralities of its two endpoints. In this paper, we present a novel way to evaluate the centrality for both nodes and edges simultaneously by constructing a mutually updated iterative framework. Furthermore, we will prove that the node centralities obtained by this framework are actually the principal eigenvector of the signless-laplacian matrix of the input network, thus we call this new node centrality method as signless-laplacian eigenvector centrality method. We test it on several classical data sets and all produce satisfying results. It is expected to have a promising applications in the future.

Methodological Framework for Understanding Urban People Flow from a Complex Network Perspective

Cities are connected by their functions and so are the spaces within cities. With the emergence of multisource data containing abundant urban residents’ mobile information and the introduction of complex network methods to urban space research, a large amount of research has focused on exploring spatial structures from spatial interaction networks. Previous studies have limitations of lacking an understanding of the urban spatial structure from a systematic perspective and have not addressed the application value of research results from the perspective of urban planning. To address this research gaps, this paper proposed an analytical framework for a multiscale urban spatial characteristics analysis that is sufficiently intuitive for understanding both the spatial structure and organization relationship from a complex network perspective based on urban resident mobility data. This framework includes the full cycle workflow from data preprocessing, the construction of people flow network, method choice, and application. Unlike other works that focus on the community identified, this analytical framework considered the spatial characteristic within the communities and the relationship between communities and then compared the research results and planned urban structure. The goal of this research was to propose an analytical framework that can be applied for the identification of urban spatial structure, division of spatial management units, assessment of the implementation effects of spatial planning, and the configuration and optimization of auxiliary public service facilities. Two weeks of mobile phone signaling data that cover the Wuhu city center area were used in the empirical analysis. The results showed that (1) there is significant spatial heterogeneity between the compactness and intensity of the people flow network, (2) the community that was identified from the people flow network is more dispersed and microscopic in space compared with the communities from master plan; and finally, (3) at the community scale, there are fewer communities in the urban north area than in other areas and the interaction between communities in the urban north area is much looser and weaker in intensity. These findings may help in understanding the organizational relationship of urban groups from a systematic perspective and provide useful information to the division of planning groups and show the effects of assessment planning implementation to the planer.

Data-driven modeling and analysis based on complex network for multimode recognition of industrial processes

An industrial process usually has multiple operating conditions or periods due to various factors such as the fluctuation of raw material quality, differences of worker levels, and changes of product specifications. Industrial data from different operating conditions or periods have different statistical characteristics, namely multimode behavior. In fact, multimode recognition of industrial processes is the first problem to develop effective intelligent manufacturing strategies for engineers. The clustering-based methods can help them characterize multimode behavior in historical databases and group them together with little detailed knowledge of the process. However, these methods are facing the challenges of massive high-dimensional industrial big data and unknown mode number. This study proposes a construction method of complex network to describe the potential relationship in industrial big data. The topological structure of complex network can be acquired by taking the correlation matrix of multi-variables as nodes and the similarity between correlation matrices as edges. On this basis, community detection technology is employed to reveal the aggregation behavior of nodes in network. It takes the maximum modularity as the optimization objective, and partitions each node into one community through greedy search algorithm, where a community denotes a mode of industrial process. Finally, case studies have demonstrated the proposed method has good performance and application prospects.

Complex network of synchronous climate events in East Asian tree-ring data

With high temporal resolution and accurate age control, tree-ring width is a good proxy for recording past climate variations from interannual to centennial time scales. The complex network method, widely used in analyses of modern meteorological observations, is an effective method to demonstrate synchronizations within climate events, thus revealing potential climate teleconnections. In this study, we tested to use the complex network in analyzing extremes recorded in time serials of tree-ring width in East Asia during the past 600 years. Our study indicates that this method is a valuable approach to reveal synchronizations in climate-sensitive tree-ring width records, though the ability of this method is dependent on the quality of the tree-ring data. Furthermore, our study shows stronger synchronizations in extremes of tree-ring width records during the CE 1850–1950 period in the Modern Warm Period than the CE 1450–1650 period in the Little Ice Age. A remarkable shift in synchronization types within the south part and the inland East Asia sites appears during the year CE 1850–1950. These changes in synchronizations suggest potential reorganizations in climate teleconnections, which is worthy being addressed in further studies with detrending and statistical significance testing methods involved.

A Complex Network Framework for Validated Assessments of Systems of Systems Robustness

The complexity of modern systems of systems (SoS) requires the ability to quickly and effectively evaluate robustness in architecture alternatives. This article presents a framework for rapid, quantitative comparisons of robustness in SoS architectures that leverages complex network methods for assessing robustness and design of experiments (DoE) techniques for validating their use in the scenario of interest. We consider both single-layer and multilayer network representations of SoS and focus on algebraic connectivity, inverse average path length, and largest connected component size as measures of robustness. Two case studies are used to illustrate our framework and assess its utility: a command, control, communications, computer, intelligence, surveillance, and reconnaissance (C4ISR) simulation and a multilayer message-passing network simulation. We find that most of the considered network metrics capture expected robustness trends, though their ability to capture these trends is often affected by the scenario of interest. These results demonstrate the potential value of complex network methods for lightweight analysis of robustness in SoS architecture alternatives, when appropriately supported by DoE methods for understanding their limitations.

The influence of regional preferential trade agreements on international manufacturing trade in value-added: Based on the complex network method.

Based on a new trade accounting method—the trade in value-added accounting method—this paper constructs the international manufacturing trade in value-added networks and preferential trade agreement (PTA) networks and uses the complex network analysis method to explore the relations between PTA and international manufacturing trade in value-added from the perspective of the global value chain. The results are as follows: (1) Over the years, the international manufacturing trade in value-added networks and PTA networks has shown a significant clustering effect, and the size of networks has grown rapidly. (2) The TEX, DVA and FVA networks of the international manufacturing value added trade over the years can be divided into two societies in the Asia-Pacific region and the European region. This division just reflects the different modes of division of labor in the manufacturing value chain of the two major economic regions in the world. (3) QAP analysis shows that the influencing factors of the traditional gravity model can still explain the manufacturing trade network and its value-added trade network, while the influence of economic globalization, the enlargement of the EU and the internationalization strategy of enterprises, the PTA network and manufacturing value-added the relationship between trade networks changed from positive to negative in 2004.

Complex network and redundancy analysis of spatial–temporal dynamic changes and driving forces behind changes in oases within the Tarim Basin in northwestern China

Oases serve as the material basis for human survival and development in arid areas, and thus monitoring their spatial–temporal dynamic changes is of theoretical and practical significance. In this study, a four-stage oasis dynamic change network model was constructed, based on the complex network method, to analyze oases dynamic changes within the Tarim Basin located in northwestern China from 1990 to 2018. Network indicators, such as the degree value, betweenness centrality, and average path length were calculated to identify key land types and transformation processes, and to discuss the stability of oasis systems. This method provided a process analysis with statistical metrics and more information than the traditional methods to reveal the spatial–temporal dynamic change process of oases. In addition, seven social and economic indicators were selected to analyze the driving force behind oasis dynamic change through detrended correspondence analysis and redundant analysis. Our results showed that the total and artificial oases areas increased, but the natural oases area decreased, during 1990 to 2018. Cultivated land and natural grassland played a key role in oasis dynamic change. The conversion between various nodes in the network became simpler and the oases systems became increasingly unstable with time. The urbanization and industrialization factors prompted the conversion of cultivated land and unutilized land to construction land, and economic indicators were the driving forces for the expansion of cultivated land. In summary, in order to establish a harmonious symbiotic relationship between humans and the natural environment, it is necessary to effectively control the scale of artificial oases conversion and protect the benign development of natural oases. Prohibiting excessive reclamation of cultivated land, returning unstable cultivated land to grassland for saving water, and governing degraded grassland were suggested to realize the sustainable oasis development.

The impact of COVID-19 on industry-related characteristics and risk contagion

We use the cutting-edge causal forest algorithm to analyze the heterogeneous treatment effects of the COVID-19 outbreak on China's industry indexes. The variable importance index is used with the causal forest and complex network methods to analyze the characteristics of industrial relations and the types of industry risk contagion before and after the COVID-19 outbreak. The results show that the heterogeneity of industries was significantly weakened during the COVID-19 outbreak. In addition, the COVID-19 outbreak changed the original structure of the industry-related network, which shifted to a star network structure with leisure services at the core. It also changed the type of risk contagion between industries, from the original middleman risk type to the input risk type.

Resilient set-membership state estimation for nonlinear complex networks with time-delay and incomplete measurements

Taking the incomplete measurements and the weighted try-once-discard (WTOD) protocol into account, this paper develops a novel resilient set-membership state estimation (RSMSE) method for time-varying nonlinear complex networks with time-invariant delay. A classic interval matrix technique is utilized to describe incomplete measurements. The Taylor series expansion is applied to dispose the nonlinearities, where the high-order terms of the linearization errors are described by norm-bounded uncertainties. To mitigate the communication burden, the WTOD protocol is introduced, where only one node can send updated data through a shared communication network at each certain transmission step. Using the recursive linear matrix inequalities (RLMIs), a series of ellipsoidal sets including the state vector can be determined. The desirable estimator gain and a smallest possible estimation ellipsoid can be calculated via solving the convex optimization problem. Lastly, we use an illustrative example to show the feasibility of the introduced RSMSE technique.

Beyond standard economic approaches: complex networks in climate finance

The financial system is a key tool to enable the shift towards a climate-smart economy: by reallocating capital to low-carbon assets, it internalizes the climate externality. However, the financial sector operates as an ecosystem of evolving agents continuously shaping the outcomes they jointly generate. Hence, the consequences of global warming and the climate impacts are potentially amplified by the micro and meso dynamics of agents interacting with each other and with technologies and institutions in the space they operate. In this working paper, we present a concise but exhaustive review about complex networks models and methods applied to climate finance. We show where networks can overcome the limitations of standard economic models in both macroprudential regulation and capital allocation. We present the main challenges ahead and we discuss the importance of a renewed research-policy dialogue to advance the discipline.

Evolutionary Characteristics of Project Interdependent Network within National Quality Award Projects of China

The research on the evolutionary law of project interdependent network is the innovation in construction industry. Based on the complex network theory and method, this research establishes the projectproject correlation network, and it is built based on the actual case of the National Quality Award Projects of China (NQAPC). We aim to systematically analyze the structural characteristics and evolution mechanism of the project interdependent network by using global network parameters. As network size increases, the degree follows power-law distribution, the average path length tends to decrease, and the clustering coefficient and heterogeneity gradually increases. The research results may help managers achieve higher performance in strategic network management of construction projects.

Brain Functional Interaction of Acupuncture Effects in Diarrhea-Dominant Irritable Bowel Syndrome.

Acupuncture is a traditional Chinese medicine treatment that has widely been used to modulate gastrointestinal dysfunction caused by irritable bowel syndrome (IBS) and to alleviate the resulting pain. Recent studies have shown that gastrointestinal dysfunction caused by IBS is associated with dysregulation of the brain's central and peripheral nervous system, while functional magnetic resonance imaging (fMRI) helps explore functional abnormality of the brain. However, previous studies rarely used fMRI to study the correlations between brain functional connection, interaction, or segregation (e.g., network degree and clustering coefficient) and acupuncture stimulation in IBS. To bridge this knowledge gap, we study the changed brain functional connection, interaction, and segregation before and after acupuncture stimulation for diarrhea-dominant IBS (IBS-D) with the help of complex network methods based on fMRI. Our results indicate that the abnormal functional connections (FCs) in the right hippocampus, right superior occipital gyrus, left lingual gyrus, left middle occipital gyrus, and the cerebellum, and abnormal network degree in right middle occipital gyrus, where normal controls are significantly different from IBS-D patients, are improved after acupuncture stimulation. These changed FCs and the network degree before and after acupuncture stimulation have significant correlations with the changed clinical information including IBS symptom severity score (r = −0.54, p = 0.0065) and IBS quality of life (r = 0.426, p = 0.038). We conclude that the changes of the brain functional connection, interaction, and segregation in the hippocampus, middle and superior occipital gyrus, cerebellum, and the lingual gyrus may be related to acupuncture stimulation. The abnormal functional connection, interaction, and segregation in IBS-D may be improved after acupuncture stimulation.

Analysing the Structure of the Global Wheat Trade Network: An ERGM Approach

This paper studies the relationship between wheat trading countries using both descriptive and statistical inference methods for complex networks. The global Wheat Trade Network (WTN) and its evolving topological characteristics is analysed for the periods 2009–2013 and 2014–2018, using the Food and Agriculture Organization (FAO) data. The network characterisation measures in both periods are rather stable. There are some differences, however, in the magnitude of some measures (e.g., connectivity or disassortativity), and a higher degree of inequality in the distribution of the number of partners and the distribution of trade volume in the period 2014–2018. An Exponential Random Graph Model (ERGM) has been applied to identify significant determinants associated with the presence/absence of trade links between countries. The results indicate that wheat trade links are driven mainly by country openness, reciprocity (mutual importer-exporter relationship), whether the exporting country is US or Canada and the geographical location of importer and exporter. Other factors, such as the economic size of the importing country, if the importing country is US or Canada and the land surface of the exporting country also contribute less to capture interlinkages of WTN.

Construction of landscape ecological network based on landscape ecological risk assessment in a large-scale opencast coal mine area

The comprehensive effect of multi-source factors changes the landscape structure and reduces the quality of landscape ecology in large-scale opencast coal mine area, resulting in an ecologically fragile environment. Landscape ecological quality directly affects the flow and transmission of ecological functions, which is an important factor in the construction of landscape ecological network. In the study, based on the Landsat remote sensing images of 1986, 1996, 2000, 2009, and 2015 in the Pingshuo opencast coal mine area of the Loess plateau in Shanxi Province of China, the landscape loss index and ecological sensitivity index were selected to establish the landscape ecological risk assessment model, and the spatial and temporal characteristics of the landscape ecological risks from 1986 to 2015 were analyzed. Considering the impact of landscape ecological quality on ecological resistance, the Minimum Cumulative Resistance model (MCR) was applied to build a landscape ecological network based on the landscape ecological risk assessment in 2015 to identify the important ecological elements, and the complex network method was used to analyze the structure of landscape ecological network. The local risk level of the study area reduced with opencast coal mining; however, the overall risk level was improved with conducting land reclamation. The high-risk area showed a trend of spreading from southwest to northeast; the risk of the peripheral areas of urban construction land and mining land was relatively high, while that of the forest and grassland area was relatively low. The risk change of the study area was mainly affected by the mining scale, urban expansion, conversion of farm land to forest and grassland, land reclamation, and village relocation. Through the complex network method, the energy flow fluency and the relationship between nodes were enhanced after the optimization of the landscape ecological network, and the ecological elements for specific land use protection were identified. The research results can provide scientific basis for the optimization of landscape pattern and the construction of ecological security pattern in Pingshuo opencast coal mine area.

Network Characteristics and Vulnerability Analysis of Chinese Railway Network under Earthquake Disasters

The internal structure and operation rules of railway network have become increasingly complex along with the expansion of the network, putting a higher demand on the development of the railway and the reliability and adaptability of the railway under earthquake disasters. The theory and method concerning complex railway network can well capture the internal structure of railway facilities system and the relationship between subsystems. However, most of the research focuses on the vulnerability based on the logical network of railway, deviating from the actual spatial location of railway network. Additionally, only random attacks and deliberate attacks are factored in, ignoring the impact of earthquake disasters on actual railway lines. Therefore, this paper built a geographic railway network and analyzed topological structure of the network and its vulnerability under earthquake disasters. First, the geographic network of Chinese railway was built based on the methods of complex network, linear reference and dynamic segmentation. Second, the spatial distribution of railway network flow was analyzed by node degree, betweenness and clustering coefficient. Finally, the vulnerability of the geographic railway network in areas with high seismic hazards were assessed, aiming to improve the capacity to prevent and resist earthquake disasters.

A Dynamic Evolution Mechanism for IoV Community in an Urban Scene

Existing work on the Internet-of-Vehicles (IoV) community mainly focuses on the detection of IoV community using static network detection and evolution methods of complex networks. These methods are prone to over-centralization, high computational complexity, and poor stability during the evolution of a community. In this work, we present an IoV community model and its evolution mechanism in an urban scene. More specifically, we first propose an IoV community detection model based on node similarity merging. Then, we use a network increment-based strategy to analyze node increment, edge increment, and weight increment. Finally, we give a dynamic evolution mechanism of an IoV community. Simulation-based experimental evaluation results show that the proposed mechanism achieves better real-time performance and accuracy than existing methods.

Path-based extensions of local link prediction methods for complex networks

Link prediction in a complex network is a problem of fundamental interest in network science and has attracted increasing attention in recent years. It aims to predict missing (or future) links between two entities in a complex system that are not already connected. Among existing methods, local similarity indices are most popular that take into account the information of common neighbours to estimate the likelihood of existence of a connection between two nodes. In this paper, we propose global and quasi-local extensions of some commonly used local similarity indices. We have performed extensive numerical simulations on publicly available datasets from diverse domains demonstrating that the proposed extensions not only give superior performance, when compared to their respective local indices, but also outperform some of the current, state-of-the-art, local and global link-prediction methods.