Characteristics Of Small-world Networks Research Articles

Synchronizability of multi-layer small-world dynamical networks

This paper investigates the synchronizability of multi-layer small-world dynamical networks and discusses the significant factors affecting the synchronizability. First, we introduce the network model of multi-layer small-world dynamical networks with one-to-one connections in the inter-layer connections and each layer has the same topology. Second, we consider the topological parameters including adding probability, intra-layer coupling strength, inter-layer coupling strength, number of nodes per layer, initial node degree, and number of network layers which will influence the network’s synchronizability. Third, we adopt the master stability function method and numerical simulations to analyze the synchronizability of the network. We also examine how the topological parameters influence the synchronizability of multi-layer small-world dynamical networks and the relationships among these parameters. Finally, synchronization control experiments are conducted to verify our results. As a result, we find that only increasing the number of nodes per layer will weaken the synchronizability of multi-layer small-world dynamical networks, while increasing other topological parameters will enhance the synchronizability. The current findings enable us to gain a deeper understanding of the synchronization behavior and characteristics of multi-layer small-world dynamical networks.

ReqNet and ReqSim: A Network and Semantic Similarity Dataset of Requirements from the Tree Structure of System Requirement Specifications

Abstract Systems are developed as a solution to the problem space defined by their requirements. The requirements are acquired during the elicitation process. The creative nature of the elicitation process, proprietary nature of requirements, the need of extensive preprocessing and the diverse techniques for analysis restricts the development of a requirement dataset. There exists no standard method to create a requirement dataset. Thus, we devise a semi-formal method to create a multi-purpose requirement dataset that harnesses human knowledge in the system requirement specification documents (SyRSDs) to facilitate the deployment of modern computing algorithms. Our dataset has three forms. (1) ReqList, a list of requirements from 86 distinct systems with their document structure in pure text form. The 12701 requirements are ready to leverage natural language processing techniques and unsupervised machine learning techniques. (2) ReqNet, a large network of requirements consisting of 17375 nodes to deploy graph-theoretic algorithms for requirement engineering. ReqNet portrays small-world network characteristics with an average distance of 9.5619 links. (3) ReqSim, a dataset consisting of 10933 pairs of requirements annotated with their similarity scores. ReqSim enables sentence-level supervised learning tasks to exploit the semantics of requirements. The similarity scores are coherent with human knowledge. Our dataset is grounded by the tree structure of SyRSDs. We devise a method to extract a tree from the SyRSDs. The tree structure resonates with the hierarchical nature of the requirement allocation process. The ReqList, ReqNet and ReqSim dataset is available at https://github.com/ChandanKSahu/ReqList_ReqNet_ReqSim.

Disrupted properties of functional brain networks in major depressive disorder during emotional face recognition: an EEG study via graph theory analysis.

Previous neuroimaging studies have revealed abnormal brain networks in patients with major depressive disorder (MDD) in emotional processing. While any cognitive task consists of a series of stages, little is yet known about the topology of functional brain networks in MDD for these stages during emotional face recognition. To address this problem, electroencephalography (EEG)-based functional brain networks of MDD patients at different stages of facial information processing were investigated in this study. First, EEG signals were collected from 16 patients with MDD and 18 age-, gender-, and education-matched normal subjects when performing an emotional face recognition task. Second, the global field power (GFP) method was employed to divide group-averaged event-related potentials into different stages. Third, using the phase transfer entropy (PTE) approach, the brain networks of MDD patients and normal individuals were constructed for each stage in negative and positive face processing, respectively. Finally, we compared the topological properties of brain networks of each stage between the two groups using graph theory approaches. The results showed that the analyzed three stages of emotional face processing corresponded to specific neurophysiological phases, namely, visual perception, face recognition, and emotional decision-making. It was also demonstrated that depressed patients showed abnormally decreased characteristic path length at the visual perception stage of negative face recognition and normalized characteristic path length in the stage of emotional decision-making during positive face processing compared to healthy subjects. Furthermore, while both the MDD and normal groups' brain networks were found to exhibit small-world network characteristics, the brain network of patients with depression tended to be randomized. Moreover, for patients with MDD, the centro-parietal region may lose its status as a hub in the process of facial expression identification. Together, our findings suggested that altered emotional function in MDD patients might be associated with disruptions in the topological organization of functional brain networks during emotional face recognition, which further deepened our understanding of the emotion processing dysfunction underlying MDD.

An empirical assessment of whether urban green ecological networks have the capacity to store higher levels of carbon

Carbon–neutral growth is a crucial long-term climatic aim in the context of global warming. This paper introduces complex network theory and explores its potential application to achieve this goal. Specifically, we investigate the spatial and temporal distribution of nodes and sources in the ecological network, and examine whether a relationship exists between the topological index of network nodes and the landscape pattern index of ecological source areas. We also determine the contribution of nodes to the carbon stock of the entire network by exploring the correlation between the carbon stock of nodes and sources to develop an optimization strategy based on the synergistic effect of node-source carbon enhancement. Finally, we test the effect of network optimization through robustness. Our results show that: (1) The correlation topological feature index analysis reveals that the degree distribution of the node network's topological characteristics becomes dispersed and modular, exhibiting the characteristics of small-world networks according to a large clustering coefficient. The heterogeneity and extent of ecological source landscapes have increased by modularity index but remain distributed and locally fragmented; (2) According to correlation analysis, by enhancing the eccentricity of the node topology, the patch cohesion index (COHESION) of the ecological source site can maximize the contribution of the node to the enhancement of the carbon stock benefits of the source site; (3) According to the tests on the robustness of nodes and edges and the robustness of network links, network stability is improved and carbon sink capacity is enhanced. Simultaneously, the restoration and rejuvenation of ecological space through national ecological construction projects can effectively improve the carbon sink within the organized region, contributing to the carbon neutrality aim. This research gives scientific and quantifiable references for potential ecological construction projects for sustainable cities and the optimization of urban ecological space structure.

Relevance Analysis of China’s Digital Industry

Currently, the digital economy has emerged as a crucial force reshaping the global competitive landscape. This study aims to comprehensively explore the extent and economic significance of the digital industry. By utilizing existing national input–output tables, an input–output sequence table for the digital industry spanning from 2012 to 2020 was constructed. Through the application of input–output analysis and complex network methods, the study analyzed the interdependent effects of the digital industry and examined the structured characteristics of the industry network. The results indicate the following: (1) The core sectors of the digital industry primarily rely on the secondary industry as a foundation, utilizing its resources to drive their development. Simultaneously, these core sectors provide support to the tertiary industry, facilitating its transformation and upgrade. (2) The digital industry plays a role as a backward-linkage sector within the industrial chain, acting as an intermediate demand-driven industry that propels the transformation and growth of other industries. (3) China’s industrial network exhibits characteristics of both scale-free and small-world networks, featuring an uneven connectivity among industry nodes. (4) The digital foundational sectors are at the periphery of the network and have not developed a core advantage. The digital core sectors have not yet become the leading industries, but they have emerged as pivotal departments within the industry system, indicating the growing dominance of the digital industry in the overall economy.

Research on the Characteristics and Influencing Factors of Provincial Urban Network from the Perspective of Local Governance—Based on the Data of the Top 100 Enterprises in Four Categories in Fujian Province

With the development of the division of labor in product value chains and the specialization of urban functions, the network link structure model among cities is being reshaped. Studying the structure of urban networks and its related theories in the context of scale, place and policy is still an open area. This study constructs a research framework to study the urban network formed by the synergy of scale, place and policy. It mainly takes enterprises in different industries in different provinces as the empirical scale and object, and uses methods such as a social network and Geo Detector to analyze the characteristics and influencing factors of the provincial network relationship mode of enterprises among cities. The main findings are as follows. (1) Firstly, the urban network linkage in general shows strong coastal centrality and small-world network characteristics. The urban network linkages reflected by different types of enterprises all have obvious spatial directionality and polarization effects. (2) Coastal cities have strong centrality, and the specialized division of urban functions emerges, with large cities becoming a concentration area for different types of corporate headquarters, while small- and medium-sized cities carry a large number of processing and assembly enterprises. (3) The networks of different types of enterprises have different sensitivity to each influencing factor. For example, emerging industries have the strongest correlation with the economic scale and social services; manufacturing industries are most influenced by the public services, administrative level and development zone level; and service industries are most influenced by science and technology expenditure and the same metropolitan area. In conclusion, this study contributes to the understanding of network heterogeneity at the provincial scale and provides policy support for the local governance scale, as well as promotes the expansion of the urban network theory to network governance applications in the “flow space”.

Knowledge Network Analysis of Domestic and Foreign Achievement Emotions Research

이 연구는 키워드 네트워크 분석 방법을 통해 국내외 성취정서 연구의 지식네트워크 간의 차이를 파악하고 이를 토대로 국내 성취정서 연구 방향을 조망하고 학문적 가치를 창출하는데 목적이 있다. 이를 위해 2001~2020년까지 국내외 학술지와 학위논문에 게재 된 성취정서 연구의 키워드를 활용한 지식네트워크 분석을 했다. 분석도구는 Netminer 4.4 프로그램을 활용하였으며 분석결과는 다음과 같다. 첫째 국내외 공통적으로 성취정서 연구의 네트워크 구조는 좁은 세상과 무척도 네트워크의 특징을 보였다. 둘째, 국내 외 공통적인 핵심주제는 성취정서, 수학, 자기효능감, 자기조절학습 등이다. 이 밖에 국내는 학업정서조절, 사회적지지, 학습몰입이, 국외는 정서, 동기, 온라인 학습이 독립적인 주제로 나타났다. 셋째, 하위그룹 분석 결과 국내연구는 6개 그룹(성취정서, 성취목표지향성, 학업성취, 척도개발, 자기효능감, 지식구조)이, 국외연구는 4개 그룹(학업성취, 성취정서, 척도개발, 통제가치이론)으로 형성되었다. 넷째 연구대상은 국내의 경우 중등, 대학, 초등 순으로 국외는 대학, 중등, 초등 순으로 나타났다. 다섯째 연구설계는 국내외 공통적으로 SEM, 회귀분석, 인과비교 순으로 나타났다. 이를 토대로 국내연구에서 부족한 다양한 학습환경, 교사관련 변인과 질적연구 확대 등 주제 확장에 대한 논의를 하였다.This study aimed to contribute to the creation of academic value by identifying the differences in knowledge networks between domestic and foreign achievement emotions studies through keyword network analysis. To this end, knowledge network analysis was conducted using the keywords of theses related to achievement emotion published in domestic and international theses and journals from 2001 to 2020. As a result of the analysis, the overall structure of domestic and foreign achievement research showed the characteristics of small-world network. Second, the common core topics at domestic and foreign are achievement emotion, mathematics, self-efficacy, and self-regulated learning. In addition, emotional control, social support, and learning flow were independent topics in Korea, and emotion, motivation, and online learning were independent topics in foreign research. Third, domestic research was formed into 6 sub groups and foreign research was formed into 4 groups. Fourth, the subjects of the study were in the order of middle school, university, and elementary school in Korea, university, middle school, and elementary school in foreign. Lastly, Research design was used as a method of research on achievement emotions in the order of SEM, regression analysis, and causal comparison in common at domestic and foreign. And based on the research results, discussions were made on the expansion of topics such as various learning environments that were lacking in domestic research, teacher-related variables, and expansion of qualitative research.

Risk spillover networks in financial markets: Evidence from emerging markets

AbstractThis paper empirically explores dynamic risk impact of extreme volatility between stock and foreign exchange markets in emerging markets. Based on CoVaR analysis, we construct networks to investigate the nonlinear and dynamic evolution characteristics of risk contagion. The results show that stock and foreign exchange markets exhibit asymmetric tail dependence. All networks present small world network characteristics. The risk tolerance impact is more noticeable than risk overflow and stock markets are more sensitive to risk contagion. In the minimum spanning tree (MST), Korean stock market and foreign exchange markets of India and China are critical nodes in the minimum path of risk overflow.

Exploring the Project-Based Collaborative Networks between Owners and Contractors in the Construction Industry: Empirical Study in China

In the project-based construction industry, organizations build collaborative relationships through specific projects. The owners and contractors who are the key project stakeholders have gradually formed a complex project-based industry-level collaborative network in many different projects, closely related to knowledge exchange and industry development. Based on the data set of the National Quality Engineering Award (NQEA) projects in China from 2013 to 2021, we empirically analyze the characteristics and evolution of project-based collaborative networks between owners and contractors in the construction industry by using social network analysis (SNA) and network motif analysis (NMA) method. The results show that (1) the owner–contractor collaborative network exhibits small-world network characteristics. The island effect caused by small groups in the network makes the overall connectivity of the network low. During the study period, the collaborative network became more compact. (2) State-owned construction companies, such as China Construction Third Engineering Bureau Corporation Limited, China Construction Eighth Engineering Bureau Corporation Limited, and China Construction Second Engineering Bureau Corporation Limited, with high degree centrality and betweenness centrality, are the core companies in the collaborative network. In China, state-owned construction enterprises are favored by owners and have established collaborative relationships with many owners and contractors. (3) There are two local collaborative patterns in the collaborative network: motif and anti-motif. Motifs include some triangle-based tight collaborative patterns, while anti-motifs involve some loose binary collaborative patterns. The results help understand the structure and evolution of the industry-level collaborative relationship network between owners and contractors and can provide references for owners and contractors to develop relationship cultivation strategies more effectively.

Analysis of cattle movement networks in Paraguay: Implications for the spread and control of infectious diseases.

Beef exports represent a substantial part of Paraguay's agricultural sector. Cattle movements involve a high risk due to the possible spread of bovine diseases that can have a significant impact on the country's economy. We analyzed cattle movements from 2014 to 2018 using the networks analysis methodology at the holding and district levels at different temporal scales. We built two types of networks to identify network characteristics that may contribute to the spread of two diseases with different epidemiological characteristics: i) a network including all cattle movements to consider the transmission of a disease of rapid spread like foot and mouth disease, and ii) a network including only cow movements to account for bovine brucellosis, a disease of slow spread that occurs mainly in adult females. Network indicators did not vary substantially among the cattle and cow only networks. The holdings/districts included in the largest strongly connected components were distributed throughout the country. Percolation analysis performed at the holding level showed that a large number of holdings should be removed to make the largest strongly connected component disappear. Higher values of the centrality indicators were found for markets than for farms, indicating that they may play an important role in the spread of an infectious disease. At the holding level (but not at the district level), the networks exhibited characteristics of small-world networks. This property may facilitate the spread of foot and mouth disease in case of re-emergence, or of bovine brucellosis in the country through cattle movements. They should be taken into account when implementing surveillance or control measures for these diseases.

Understanding Hazardous Materials Transportation Accidents Based on Higher-Order Network Theory.

In hazardous materials transportation systems, accident causation analysis is important to transportation safety. Complex network theory can be effectively used to understand the causal factors of and their relationships within accidents. In this paper, a higher-order network method is proposed to establish a hazardous materials transportation accident causation network (HMTACN), which considers the sequences and dependences of causal factors. The HMTACN is composed of 125 first- and 118 higher-order nodes that represent causes, and 545 directed edges that denote complex relationships among causes. By analyzing topological properties, the results show that the HMTACN has the characteristics of small-world networks and displays the properties of scale-free networks. Additionally, critical causal factors and key relationships of the HMTACN are discovered. Moreover, unsafe tank or valve states are important causal factors; and leakage, roll-over, collision, and fire are most likely to trigger chain reactions. Important higher-order nodes are discovered, which can represent key relationships in the HMTACN. For example, unsafe distance and improper operation usually lead to collision and roll-over. These results of higher-order nodes cannot be found by the traditional Markov network model. This study provides a practical way to extract and construct an accident causation network from numerous accident investigation reports. It also provides insights into safety management of hazardous materials transportation.

Investigation on the Distribution Characteristics of Chinese Continuing Education Based on the Community Detection Algorithm in Complex Networks.

In order to closely fit the characteristics of continuing education, the development of continuing education teaching activities under the network background should not only be combined with the characteristics of professional adult education but also make reasonable use of modern teaching models in the actual teaching process. Based on the community detection algorithm in complex networks, this article makes thorough research and analysis on the complexity of Chinese continuing education by using complex network technology. By establishing the characteristics of vertex degree distribution, average path length, and clustering coefficient of complex networks, it is confirmed that Chinese continuing education has scale-free network characteristics and small-world network characteristics. The three aspects of relationship strength comprehensively analyze the information dissemination speed, scope, interpretation, and application; through the combination of the ant colony algorithm and complex network technology, multiple information dissemination paths are abstracted in Chinese continuing education. The research shows that the application of complex network algorithms can effectively improve the speed and quality of continuing education in China. It is found that the government should increase the number of adult education projects and improve the level of project categories, form key adult education research basis to promote the diversification of research subjects, expand the space for adult education projects to balance regional and provincial differences and attach importance to basic research on adult education, and integrate applied research.

Global tourist flows under the Belt and Road Initiative: A complex network analysis.

This study applies complex network analysis to examine global tourist flows network in the context of Belt and Road Initiative (BRI). Using tourist flows data between 221 countries/regions over 1995–2018, we investigate the nature and development patterns of structural properties of global network as well as factors influencing its formation. The descriptive analysis indicates that global tourist network was a sparse network with small world network characteristics. According to centrality characteristics, China showed the most influence in the BRI group, while Germany and the United States possessed key roles among non-BRI countries/regions. Exploratory analysis demonstrated significant influence of gravity variables in global, BRI and non-BRI tourist networks. This research advances existing tourism theory and provides practical implications for policymakers.

PROPIEDADES DE RENDIMIENTO Y ANÁLISIS DE AGRUPACIÓN DE LOS PUERTOS COSTEROS DE CHINA CONTINENTAL: UN MÉTODO DE ANÁLISIS MEDIANTE UNA RED COMPLEJA DE SERIES TEMPORALES

Explicit development orientations of ports are critical to promoting port clusters from chaotic competitions to a hierarchical collaborative development. To investigate the development orientation and hierarchies of coastal ports in Mainland China, a visibility graph network was constructed in this study for a time series analysis of China's port throughputs from 2000 to 2020. In this model, the four throughput indexes of cargo, domestic trade cargo, foreign trade cargo, and container were used to measure the scale of ports. The topological structural features of the throughput network of ports, including its average degree, diameter, and average clustering coefficient, were calculated. The ports were then divided into four types based on their network characteristics. Results show that the throughput time series network of coastal ports in Mainland China has an obvious hierarchical structure and generally has the characteristics of small-world networks. These ports are closely connected and have a strong transshipment capacity. However, an "absolute center" does not exist in this network, and these ports are in a state of disorderly and chaotic competition with serious homogenization. The proposed model provides an appropriate method for the future planning and design of port clusters. Keywords: Port throughput, Throughput characteristics, Visibility graph, Time series complex networks, Clustering analysis

Global Trade Pattern of Traditional Chinese Medicines and China's Trade Position.

To depict the evolution of the global trade of traditional Chinese medicine (TCM) products, this article analyzes the 2001–2020 trade data of TCM products in the World Bank and United Nations Commodity Trade Statistics Database to discern the spatial-temporal evolution characteristics of global and China's trade patterns of TCM products from 2001 to 2020 and thereby assess the changes in the global trade of TCM products and in the positions of various countries or regions in the global trade of TCM products. Research findings are as follows: First, on the whole, the total trade volume of TCM products and the number of participating economies and trade connections are on the rise. Second, in terms of topological structure, with higher network density and rising transmission efficiency, the global trade network of TCM products has typical small-world and scale-free network characteristics and has begun to be controlled by a few countries. Judging from the co-opetition between major trading countries, there are more diversified sources of imports for major trading countries, and there is competition between supplying countries. Third, For China, the trade volume of TCM products between China and various countries worldwide has grown rapidly and exhibits a trend of continuous increase followed by decline. China has established extensive trade partnerships and its position in the global trade network of TCM products has been continuously improved. China's participation has contributed to a closer connection among trading entities, but its network heterogeneity remains to be further improved. From the perspective of trade interdependence, the number of countries or regions maintaining high interdependence with China has been gradually increasing, and most of them are European and American countries, Japan, and Southeast Asian countries. The number of countries or regions maintaining low interdependence with China has gradually decreased, and countries or regions that are completely one-way dependent on China are nonexistent.

Analysis of Brain Functional Network Based on EEG Signals for Early-Stage Parkinson’s Disease Detection

The early diagnosis of Parkinson’s disease (PD) has always been a difficult problem to be solved clinically. At present, there is no clinical auxiliary diagnostic index for reference. We attempted to extract potential biomarkers for early PD from the currently used scalp EEG detection methods in clinical practice. We calculated the phase synchronization index to quantify the synchrony of EEG channels in various frequency bands (delta, theta, alpha and beta bands) of early PD. The results showed that the synchronization of early PD in the delta band was significantly lower than the healthy level, and the brain region reflecting the lower synchronization was located in the temporal lobe, the posterior temporal lobe, the parietal lobe (the posterior center) and the occipital lobe. Moreover, this lower synchronicity is consistent with weaker brain functional connections. Besides, by constructing functional brain network, the graph theoretic topological features of each frequency band of early PD are presented. We have found that early PD has characteristics of small world network in the delta and beta bands, and functional integration and separation characteristics of brain network in early PD are significantly abnormal in the delta, theta, alpha and beta bands. These results indicate that early PD has significant pathological changes from the perspective of brain function network analysis, and its characteristics can be described by multiple features, which may provide auxiliary guidance for the clinical diagnosis of early PD, and also provide theoretical support for the brain function changes of early PD.

Research on Dynamic Characteristics of Stock Market Based on Big Data Analysis

The stock market is a real and continuously evolving extremely complex dynamic system. This paper analyzes the change of stock market efficiency from the perspective of dynamic evolution, and the recursive graph method is used to obtain the dynamic characteristics of stock price time series. For the sharp rise and fall of stock prices, this paper uses the heuristic segmentation algorithm of nonlinear time series mutation detection to study the detection of market dynamics characteristics before the stock market crash. Based on the above research results, this paper studies the dynamic evolution of financial markets and the construction of a complex network of dynamic characteristics between financial markets. The simulation results show that there are typical characteristics of small world network in stock market complex network, the stock market complex network shows stronger synchronization ability, and the transmission range of information between the stock markets in the complex network is significantly expanded.

Analyses of Contact Networks of Community Dogs on a University Campus in Nakhon Pathom, Thailand.

Free-roaming dogs have been identified as an important reservoir of rabies in many countries including Thailand. There is a need for novel insights to improve current rabies control strategies in these countries. Network analysis is commonly used to study the interactions between individuals or organizations and has been applied in preventive veterinary medicine. However, contact networks of domestic free-roaming dogs are mostly unexplored. The objective of this study was to explore the contact network of free-roaming dogs residing on a university campus. Three one-mode networks were created using co-appearances of dogs as edges. A two-mode network was created by associating the dog with the pre-defined area it was seen in. The average number of contacts a dog had was 6.74. The normalized degree for the weekend network was significantly higher compared to the weekday network. All one-mode networks displayed small-world network characteristics. Most dogs were observed in only one area. The average number of dogs which shared an area was 8.67. In this study, we demonstrated the potential of observational methods to create networks of contacts. The network information acquired can be further used in network modeling and designing targeted disease control programs.

Speeding Up Velocity Consensus Control with Small World Communication Topology for Unmanned Aerial Vehicle Swarms

This study addressed a problem of rapid velocity consensus within a swarm of unmanned aerial vehicles. Our analytical framework was based on tools using matrix theory and algebraic graph theory. We established connections between algebraic connectivity and the speed of converging on a velocity. The relationship between algebraic connectivity and communication cost was established. To deal with the trade-off among algebraic connectivity, convergence speed and communication cost, we propose a distributed small world network construction method. The small world network characteristics expedite the convergence speed toward consensus in the unmanned aerial vehicle swarm. Eventually, our method greatly sped up the consensus velocities in the unmanned aerial vehicle swarms at a lower communication cost than other methods required.

A study on the evolution of port logistics network based on complex theory: A case study of key coastal ports in China

Based on the complex network theory, we construct a weighted network evolution model with limited point weight considering node attractiveness and shipping distance. After this the evolution simulation is carried out based on the data of 18 coastal ports mentioned in the Belt and Road Initiative, and the validity of the model is verified by comparing the net-work eigenvalues between evolving network and real network. The results show that the shipping network has the characteristics of small world and scale-free network, and the evolutionary network has shorter average path, stronger agglomeration, greater degree difference, and more frequent and efficient network connection. Based on the evolving network, the position of Lianyungang as a hub needs to be strengthened. For ports with small network values, such as Shantou, Qin-zhou and Sanya, it is necessary to expand trade between ports. Finally, we also provide strategies for improving China’s key coastal port networks from the perspective of port enterprises, shipping enterprises and government.