Complex Network Perspective Research Articles

Uncovering the spatiotemporal motif patterns in urban mobility networks by non-negative tensor decomposition

Investigating the macroscopic mobility laws of the population and the microscopic travel characteristics of individuals in a city offers an essential way of understanding the city as a complex system. Research that combines the travel laws of the population and individuals simultaneously poses a challenging and complicated task. In this paper, a 4-dimensional tensor is created to describe the spatiotemporal characteristics of people’s various mobility motifs. Non-negative tensor decomposition is used to identify the principal patterns of time, space, and daily motifs in a city and the level of interactions between them. Moreover, the major network indicators of the human mobility networks constructed in terms of three principal motif patterns are statistically analyzed and calculated from the complex network perspective. Relying on a conjoint analysis of core tensor and network indicators, we find that the three motif patterns correspond to three real-life mobility scenarios: simple motifs dominated by commuting, complex motifs based on more complicated life and entertainment activities, and single trips departing from or arriving at airports or railway stations, respectively. Furthermore, the intra-city mobility networks constituted based on the three motif patterns differ significantly in network heterogeneity, node importance, entropy, and clustering coefficient. This suggests that the existing studies on intra-city human mobility networks that only consider first-order trips may be the average results of aggregations containing many people with different characteristics. In our work, the specific research on the spatiotemporal characteristics of people with different mobility motif patterns can assist policy makers in conducting fine-scale management and implementing specific policies.

Resilience of space information network based on combination of complex networks and hypergraphs

The space information network provides global users with high-speed information services that can be accessed anytime, anywhere. Its enormous economic, social and military value makes it vulnerable to large-scale targeted cyber attacks. Therefore, enhancing the resiliency of services is critical. We propose to solve the above problems from the perspective of complex networks and hypergraph fusion. A space information network model integrating complex networks and hypergraphs is constructed. Edges represent physical links between entities, and hyperedges represent information services logically composed of multiple entities. In the new model, the network efficiency within each service is comprehensively considered, and a more reasonable new measure for evaluating network service capability is proposed, called service efficiency. In the vulnerability stage of resilience, a local edge efficiency-betweenness removal strategy is proposed to identify important edges in the network, which can preserve the most performance by hardening them before an attack occurs. When the network suffers an inevitable attack, the recovery strategies of the central node based on global information and community information are proposed to restore the network performance with the fewest components. The former considers the degree of nodes in the entire hypergraph, and the latter considers the more closely connected hypergraph community structure. Simulation results under five sets of network parameters show that all our schemes apply in all cases. Hardening the links elected by the local edge efficiency-betweenness strategy in advance can enhance the network’s survivability. When the network is damaged, the recovery strategy of the central node based on global information is better under single-step recovery, while the recovery strategy of the central node based on community information is better under synchronous recovery. Using the proposed hardening and recovery strategies together, resiliency can be improved by up to 160%.

Analysis Model Design on the Influencing Factors and Countermeasures of China’s International Trade Facing the Belt and Road

Since the “Belt and Road” Initiative was put forward in 2013, it has attracted more and more attention. Many countries along the Belt and Road have participated in it, which has further deepened regional economic integration. Complex network analysis is an interdisciplinary method, which has a perfect overall perspective and data indicators that can quantify the relationship. More and more scholars have begun to pay attention to it and use it to carry out research. Based on the perspective of complex networks, this paper constructs the “Belt and Road” directed weighted network, respectively, according to the import and export trade data of countries and international organizations that have signed cooperation documents, and then studies the characteristics of the “Belt and Road” trade network by using a variety of network topology attribute analysis methods and node centric indicators.

Networked Knowledge and Complex Networks: An Engineering View

Along with the development of information technologies such as mobile Internet, information acquisition technology, cloud computing and big data technology, the traditional knowledge engineering and knowledge-based software engineering have undergone fundamental changes where the network plays an increasingly important role. Within this context, it is required to develop new methodologies as well as technical tools for network-based knowledge representation, knowledge services and knowledge engineering. Obviously, the term “network” has different meanings in different scenarios. Meanwhile, some breakthroughs in several bottleneck problems of complex networks promote the developments of the new methodologies and technical tools for network-based knowledge representation, knowledge services and knowledge engineering. This paper first reviews some recent advances on complex networks, and then, in conjunction with knowledge graph, proposes a framework of networked knowledge which models knowledge and its relationships with the perspective of complex networks. For the unique advantages of deep learning in acquiring and processing knowledge, this paper reviews its development and emphasizes the role that it played in the development of knowledge engineering. Finally, some challenges and further trends are discussed.

Analysis on mechanical properties and evolution of mesostructure of debris flow from complex network perspective

Analysis on mechanical properties and evolution of mesostructure of debris flow from complex network perspective

Evaluation of the Sustainable Development of the Social-Economic-Natural Compound Ecosystem in the Guangdong-Hong Kong-Macao Greater Bay Area Urban Agglomeration (China): Based on Complex Network Analysis

In the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), a series of natural environmental, economic, and social issues have emerged sequentially in the process of rapid economic and social development. Therefore, for the sustainable development of the GBA, how to closely integrate nature protection with economic and social development to improve the sustainable development level of the social-economic-natural compound ecosystem, and realize the coordinated development of the system is particularly important. Based on the perspective of complex network and the theory of compound ecosystem, this study proposes a set of sustainable development evaluation model based on complex network modeling to evaluate the sustainable development level of compound ecosystem in GBA from 2014 to 2018, and further analyze the coupling coordination degree. The major findings include: 1) For the sustainable development in the GBA, the development of the natural subsystem is an important foundation, and the synchronous development of the social and economic subsystems are the main driving force. 2) The sustainable development level in the GBA shows an overall steady upward trend; the average level of the compound ecosystem’s coupling coordination development is in a “good” state, and it shows an evident upward trend. 3) Whether it is within the GBA or the GBA and its surrounding regions, there are problems of unbalanced and insufficient regional development. Policy recommendations include increasing the emphasis on the sustainable development of the natural subsystem, promoting the coordinated development of the economic, social, and natural subsystems, and promoting the balanced development of cities within the GBA, as well as the GBA and surrounding regions.

The choice of Madrid as the capital of Spain by Philip II in the light of the knowledge of his time: A transport network perspective.

The suitability of Madrid as the capital of Spain is analyzed from different perspectives, questioning the belief that this choice was eminently personal or political but lacked economic rationality. The paper analyzes Madrid's advantages over other possible capitals from the point of view of both intrinsic characteristics and those that depend on the transport network, such as the problem of supplies or the impact on the development of the surrounding territory. To deal with these questions it is necessary to consider logistical aspects that require an adjusted view of the existing transport network at that time. Using little-known primary sources and a novel methodology based on Delaunay triangulation, the 16th century Spanish transport network is reconstructed with a much higher level of accuracy than ever before. With this information, two maps are prepared that could be used for logistical analysis from a complex network perspective. The first map evaluates the real effects of the choice using an adjusted representation of the territory whilst the second map aims at avoiding the common fallacy of judging decisions made in the past applying current geographical know-how. This map, constructed with the planimetry of the 16th century, indicates how the somewhat deficient knowledge of Philip II with respect to the geographical reality of the day may have favored the choice of Madrid over Toledo, converting some Mediterranean coastal cities into more attractive options. The choice of Madrid as capital appears to be very reasonable in view of the different criteria used. Regarding supply difficulties, our results depart from traditional inclinations by deliberating the fact that the absence of a port in Madrid does not pose an insuperable problem. The latter is the case given that the advantages of maritime transport are far fewer than those usually considered, with Madrid's geographical position offering significant advantages in terms of road transport.

Preferential attachment, R&D expenditure and the evolution of international trade networks from the perspective of complex networks

From the perspective of complex networks, whether international trade networks (ITNs) can remain robust and connected in the face of random emergencies or intentional attacks is an important indicator of the security of international trade. Thus, we introduced the scale-free property to describe it. Using trade data from 1996–2018 for 126 economies, we constructed ITNs and investigated their scale-free property. Our results reveal that ITNs are not scale free in most years and that they become decentralized. In addition, our study explored the evolution of ITNs using the temporal exponential random graph model (TERGM) and verified the nonexistence of the preferential attachment mechanism that drives the formation of scale-free networks. Furthermore, we emphasized the impact of different R&D expenditure levels on the formation of trade relationships. Economies with medium (low) levels of R&D expenditures are less connected to those with the same level, while economies with high levels of R&D expenditures prefer to establish trade relationships with those with the same level. This finding indicates that the establishment of trade relationships could occur more often between economies with a high level of R&D expenditures and economies with a medium (low) level of R&D expenditures or among economies with a high level of R&D expenditures. Therefore, economies with a high level of R&D expenditures are more active and attractive when selecting trade partners. Overall, we extend the exploration in the field of ITN research and provide an innovative analysis framework to study real networks.

Congestion Recognition of the Air Traffic Control Sector Based on Deep Active Learning

The air traffic control sector (ATCS) is the basic unit of the airspace system. If we can identify the congestion of an ATCS, it will help provide decision support for planning and daily operations. However, current methods mainly characterize congestion from the static structure and the dynamic operational features, resulting in poor generalization and operability. To this end, we propose a deep learning method from the perspective of complex networks. It takes aircraft as nodes to construct an aircraft network and utilizes the complexity indices to characterize it. So, the problem of identifying congestion becomes the complexity of the aircraft network. Inspired by active learning methods, we construct a deep active learning (DAL) model for congestion recognition. It adopts an iterative semi-supervised approach to reduce the number of labeled samples while ensuring recognition performance. To make full use of a large number of unlabeled samples, the sparse autoencoder is employed to characterize all labeled samples and unlabeled samples. The hidden layer of the deep neural network is constructed by stacking. In the process of active learning iteration, minimum confidence, marginal sampling, and information entropy are introduced as measures to select samples from the unlabeled sample set with significantly different features from the labeled sample set. The model is applied to three representative sectors in China’s airspace as cases. Results suggest that DAL can reduce the labeled sample set’s redundancy and achieve the desired performance with the smallest number of samples. Additionally, DAL is superior to the existing mainstream methods in the four objective evaluation indices.

A study of highway logistics transportation network structure in China: From the perspective of complex network

A study of highway logistics transportation network structure in China: From the perspective of complex network

Anti-Risk Ability of Government Purchasing Elderly Care Services under the Coordination of Complex Network and GERT

Risk problems of government purchasing elderly care services occur frequently. In order to open the “black box” of decision-making regarding the risks, this study explores the anti-risk ability of government purchasing elderly care services from the perspective of complex network and graphical evaluation review technique (GERT). Firstly, network of government purchasing elderly care services is established, which consists of link level and level of risk factors. Secondly, GERT is employed to analyze the anti-risk ability of the link level of the policy. Then combined with complex network theory, propagation process of risks is simulated at the level of risk factors and anti-risk ability of the complex network consisting of risk factors is explored. Results show that the anti-risk ability of each link in government purchasing elderly care services is obtained, which provides the priority for managers to conduct the risk management. And compared with the ani-risk ability under the random risk attack and deliberate risk attack, the anti-risk ability under the risk attack based on links is obviously lower.

Evolvement of international mobility of talents: a complex network perspective

PurposeTalents are the critical resource of sustainable development for countries, and attracting talents all over the world is the important way for increasing the country's talent reverse. Therefore, the purpose of this paper is to make the governments understand the evolving pattern of international talent mobility.Design/methodology/approachThis paper constructs the networked model of talent mobility among 15 major countries during the years 1990–2012 under the database of the Patent Cooperation Treaty and analyzes the global characteristics and the evolvement of international mobility of talents under complex network perspective.FindingsIt is found that the mobility network is scale-free, which means that a few developed countries, such as the USA, England and France, have enjoyed considerable migratory flows of talents, but most of the other countries only have moderate levels of brain circulation. Besides a very few exceptions, a dynamic equilibrium is reached for most countries, with numbers of talents evening out between emigration and immigration. As time passes by, the volume of talent flows is gradually stabilized, with the numerical gap existing between developed countries and developing ones. Nowadays, the scale of international mobility of talents is growing at an unprecedented speed, facilitated by technological progress and more flexible migratory policies.Originality/valueThis paper sheds light on the evolving pattern of the international talent mobility using complex network analysis. The cogent quantitative analysis highlights the huge challenges faced by national governments and provides the basis for deliberative policy-making aimed at winning the brain war internationally.

Mesostructural characteristics and evaluation of asphalt mixture contact chain complex networks

The skeleton structure of asphalt mixture is the main carrier to resist load. At present, the identification and evaluation of skeleton contact chains need to be further investigated. This study quantitatively analyzes the mesostructural characteristics of asphalt mixture skeleton contact chains from the perspective of complex networks by using digital image processing (DIP) technique. A self-developed meso-structure intelligent identification software is developed, the evaluation parameters of the contact chain complex networks are proposed, and five types of asphalt mixture skeleton contact chains are compared. Additionally, the multi-scale relationship between the meso and macro parameters of the contact chains has been established, and a design flow chart of the asphalt mixture skeleton mesostructure based on rutting prediction has been proposed. The results indicate that the skeleton structure of the asphalt mixture has the characteristics of a scale-free network, and the degree distribution of each coarse aggregate is inhomogeneous, exhibiting a good power-law distribution; the length of the aggregate to aggregate contact chains conforms to the normal distribution, and the asphalt mixture skeleton clustering coefficient is close to zero; the smaller the skeleton modularity of the asphalt mixture, the greater the number of coarse aggregates within the longest contact chain and the fewer the modules, indicating that the asphalt mixture can form a more perfect skeleton structure. The measures to improve the skeleton performance of asphalt mixture include focusing on the passing rates of the 2.36 and 4.75 mm aggregates, obtaining a maximum amount of coarse aggregate in the contact chains, and achieving a smaller number of modules and modularity in the network. This study presents a fast and effective technical evaluation tool and detection method for asphalt mixture indoor design and pavement construction quality detection and also provides guidance for the fine design and performance improvement of asphalt mixtures.

Research on Early Warning and Forecasting System of Public Health Emergencies Based on Complex Network

It is an important research field related to the national economy and the people’s livelihood to establish and improve the crisis early warning management of public health emergencies and improve the timeliness and accuracy of prediction and early warning. The outbreak and spread of infectious diseases is a typical complex system composed of etiology, host, and environment. From the perspective of complex network, this paper combines infectious disease dynamics with biostatistics and simulation, analyze the transmission characteristics and process of infectious diseases on complex networks, and simulate the implementation effect of various prevention and control measures. It seeks the optimal strategy for its prevention and control, so as to provide decision-making basis for the study of infectious disease emergencies. And it simulates the evolution process of social contact network driven by people's daily behavior. The results show that the transmission speed of infectious diseases in home networks is significantly lower than that in public networks. Through simulation analysis and effect evaluation, good results have been achieved, which can provide accurate and rapid decision-making for emergency managers. It proves the feasibility of the model. This study provides a new research perspective for infectious disease prevention and control.

Software Bug Number Prediction Based on Complex Network Theory and Panel Data Model

Accurate software bug number prediction makes software test resource allocation, maintenance, and release time cost efficient. However, it is a challenge to accurately predict the number of software bugs when there fluctuations caused by many uncertain factors faced by the complex software. Considering this, a new method for software bug number prediction based on a panel data model from the perspective of complex networks is proposed in this article. Using complex network theory, we constructed the software code network and calculated the static metrics of the network structure, and the percolation threshold of change in the network structure based on percolation theory as a dynamic metric. These network metrics were then normalized as inputs and a panel data model was used for bug prediction. The proposed method can predict the number of bugs for both within-project and cross-project. Empirical studies were performed on data obtained from 120 releases of eight open-source software projects (Lua, SQLite, Redis, Linux kernel, ant, jmeter, poi, and tomcat), the experimental results indicated that network metrics are effective bug indicators, and the proposed method outperformed ten baseline methods (with an average improvement of 28.05%). This article is expected to provide insights into more smart software quality and reliability assurance.

A complex network perspective on embodiment of air pollutants from global oil refining industry

The air pollutants emitted from oil refining industry could be transferred across borders through the increasingly complex global trade network. However, the specific structural features of the network remain unclear. Based on the Multi-regional Input-output method and complex network theory, we make a first attempt to trace six oil refining air pollutants embodied in the international trade. The results show that the overall character of the global oil refining air pollutants flow network exhibits small-world behavior, and each node of the network is strongly connected. Therefore, the refining emissions mitigation measures of one node could efficiently radiate to the other nodes connected to it, which provides essential opportunities for collaborative emissions reduction among countries. Besides, the individual characteristics of each node are distinguished, several key nodes dominate the embodied emissions throughout the global oil refining air-pollutants flow network. For specific countries, the United States, China, Japan, and the United Kingdom are the hub economics in importing embodied pollutants in the network, while Russia and Canada are the key exporters. Similarly, the critical paths with large flow still come from the trade between these key nodes. Our estimated results have great policy implications for reducing air pollutants emitted from oil refining industry and also have profound implications for environmental regulation and protection in the world.

Altered Functional Network in Infants With Profound Bilateral Congenital Sensorineural Hearing Loss: A Graph Theory Analysis.

Functional magnetic resonance imaging (fMRI) studies have suggested that there is a functional reorganization of brain areas in patients with sensorineural hearing loss (SNHL). Recently, graph theory analysis has brought a new understanding of the functional connectome and topological features in central neural system diseases. However, little is known about the functional network topology changes in SNHL patients, especially in infants. In this study, 34 infants with profound bilateral congenital SNHL and 28 infants with normal hearing aged 11–36 months were recruited. No difference was found in small-world parameters and network efficiency parameters. Differences in global and nodal topologic organization, hub distribution, and whole-brain functional connectivity were explored using graph theory analysis. Both normal-hearing infants and SNHL infants exhibited small-world topology. Furthermore, the SNHL group showed a decreased nodal degree in the bilateral thalamus. Six hubs in the SNHL group and seven hubs in the normal-hearing group were identified. The left middle temporal gyrus was a hub only in the SNHL group, while the right parahippocampal gyrus and bilateral temporal pole were hubs only in the normal-hearing group. Functional connectivity between auditory regions and motor regions, between auditory regions and default-mode-network (DMN) regions, and within DMN regions was found to be decreased in the SNHL group. These results indicate a functional reorganization of brain functional networks as a result of hearing loss. This study provides evidence that functional reorganization occurs in the early stage of life in infants with profound bilateral congenital SNHL from the perspective of complex networks.

Distributive hydraulic engineering, cross‐scale landscape planning, and climate change resilience: On the water‐adaptive strategy in the Huai'an–Yangzhou Section of China's Grand Canal

AbstractWater system plays a key role in the coupled human‐nature system, and ancient people's experiences to build a water‐adaptative way of life still shed light on human's pursuit for climate resilience today with their innate traditional ecological wisdom. In this paper, taking the Huai'an‐Yangzhou Section of China's Grand Canal, and particularly its hydraulic engineering system as an example, we show the system's success in addressing the multi‐dimensional challenges in a historical geographical context, as well as its landscape‐shaping power across the entire lower Yellow‐Huai River Basin up until today. Therefore, we argue that the system is not merely a hydraulic engineering project, but a cross‐scale adaptive strategy that has deeply impacted generations of people's way of livelihood with its intrinsic resilient nature. We attribute the success to the advantages brought about by the “distributive hydraulic engineering” approach, which is reflected in the ancient Grand Canal‐management, over the “master‐structure dependent” approach which appears to be the common practice in modern hydraulic engineering. For testing the hypothesis, we build complex system models representing the two approaches with the Qingkou Hydraulic Nexus as an example, and by running long‐term simulation, we evaluate the performance of the two approaches with respect to long‐term resilience. We show from a complex network perspective that the former has significant flexibility advantages, and outperforms the former in multi‐goal fulfillment capacities and long‐term resilience especially under a low‐tech, tight resource constraints condition. We conclude the paper with discussions on the implications of the findings, and argue that though with limitations, the traditional ecological wisdom as reflected in the Grand Canal case, with its holistic approach and flexibility design, still sheds lights on today's large‐scale hydraulic engineering projects that pursue a resilient solution for long‐term adaptive survival for the mankind.

A modified connectivity link addition strategy to improve the resilience of multiplex networks against attacks

Current studies have shifted from the analysis of single-layer network to multi-layer network. However, the performance of multi-layer networks has been significantly influenced by inevitable disturbances; therefore, the resilience of such networks must be maintained. Previous studies on the resilience of multi-layer networks have mainly focused on interdependent networks rather than on multiplex networks, which means that the effectiveness of current methods to improve the resilience of multiplex networks remains limited. To solve these issues, this study develops a two-layer multiplex network and describes failure propagation models by motifs in single-layer and multiplex networks. An approach to evaluate the resilience of multiplex networks is proposed, followed by strategies for its improvement. In particular, an extensive investigation into the effectiveness of connectivity link addition strategies to protect against degree-based targeted attacks is presented. A modified connectivity link addition approach based on degree deviation is proposed to find an optimal strategy under cascading failure to maintain the resilience of multiplex networks from a complex network perspective. Finally, some numerical examples and a real case study of EU-AIR multiplex networks are presented to illustrate the effectiveness of the proposed approach.

Liquidity Commonality Effect in Stock Market and Risk Correlation from the Perspective of Complex Network

This paper uses a complex network method to study the liquidity changes and liquidity commonality effects of the Chinese stock market in the two crises of 2015 and 2020. Liquidity indicator and liquidity commonality indicator-the average weighted degree are calculated to revel the risk characteristic of the two crisis. By comparing the two crisis, main conclusions are obtained: Highly leveraged transactions and excess macro-liquidity may lead to the good liquidity condition and the stronger commonality effect in 2015; Besides, the average weighted degree better reflects the systemic risks in a way of revealing the liquidity risk faced by the system.