Network Structure Characteristics Research Articles

Digital trading networks and competitive advantage in a buyer-seller network

PurposeFirms embedded in networks of relations are theorized through Gnyawali and Madhavan’s (2001) (G&M) structural embeddedness model to gain competitive advantage from topological characteristics. Empirical studies to support their theory have never been executed in full. Our study provided a full empirical test of their model in a digital trading network to achieve a higher degree of certainty that those network structural characteristics can have a major impact on the degree to which certain firms lead to competitiveness in a digital trading network environment.Design/methodology/approachTo examine how firms respond in competitive situations, we chose the hyper-active digital trading network, eBay as our empirical context. We used eBay auction data to analyze how the network characteristics of eBay resellers impact their competitive behaviors.FindingsOur study found strong support for the G&M model of competitiveness. We offer explanations for where support was not as strong as the Gynawali and Madavan theory proposes.Research limitations/implicationsOur research is limited by our chosen context and findings in support of part of G&M model. Future studies in other digital contexts are needed to enhance the modeling of network topologies and further study the impacts of network density and structural autonomy on competitive action.Practical implicationsOur study suggests that managers proceed cautiously in forming partnerships, weighing circumstances where the firm can find itself with increased information power and avoiding, to the greatest extent possible, situations where the playing field is roughly equal.Social implicationsTheory-making in this domain has begun as well as initial empirical testing. Much more needs to be accomplished, though, before embeddedness modeling can be thought of as being well established.Originality/valueThe G& M Model of competitiveness is an SNA explanation of why some competitive units succeed and others do not. Our study is the first, full blown empirical analysis of the theory.

A Novel Approach for Dynamic Analysis of Wind-Integrated Multi-Machine Power Systems

Indeed, the rapid expansion of Renewable Energy Sources (RES) in recent years has brought about numerous benefits, including reduced carbon emissions, increased energy independence, and the creation of new economic opportunities. However, integrating these variable and intermittent sources into existing power systems poses several challenges for power system management. Faults in electrical networks are among the key factors and sources of network disturbances. Control and automation strategies are among the key fault clearing techniques responsible for the safe operation of the system. In recent years, the increasing penetration of wind energy in multi-machine power systems has posed unique challenges to power grid stability and reliability. Accurate assessment methodologies are required to ensure the effective integration of wind energy sources while maintaining grid stability. Several researchers have revealed various constraints of control and automation strategies such as a slow dynamic response, the inability to switch the network on and off remotely, a high fault clearing time and loss minimization. It's important to note that the impact of wind energy on system inertia is a complex and dynamic aspect of power system operation. Ongoing research and technological advancements aim to improve the integration of wind and other renewable energy sources while ensuring grid stability and reliability. As the energy transition continues, addressing these technical challenges is crucial for building a sustainable and resilient power system. In this paper, the influence of doubly-fed induction generator (DFIG) penetration is analyzed to examine the transient stability of power system networks. The concept of a Coupling Strength Index (CSI) derived from Network Structural Characteristics Theory sounds intriguing, especially in the context of identifying critical elements susceptible to the impact of a three-phase fault in a network. The investigation involves studying the transient stability of a power system under different conditions, specifically with and without doubly-fed induction generators (DFIGs) connected to a weak bus. Additionally, a three-phase fault is applied at the middle of the identified weakest line for both the IEEE 9 and 39 bus systems. The investigation of generator speed, rotor angle, and electric power during transient stability analysis provides a holistic view of how a power system responds to disturbances. This information is crucial for ensuring the reliability and stability of the system, especially when studying the integration of renewable energy sources and addressing potential challenges associated with faults and weak buses. This paper presents a pioneering non-iterative framework for dynamically assessing wind energy dominated multi-machine power systems. The proposed framework aims to address the shortcomings of traditional iterative methods, providing a more efficient and reliable approach to power system analysis.

Diffusion of “dual carbon” policies among Chinese cities: A network evolution analysis

Numerous regions across China have introduced “dual carbon” policies, yet the response of cities to these policies remains unclear. In this study, we used emission data from 271 Chinese cities for the years 2010, 2015, and 2020 to construct a complex network of urban carbon emissions with a modified gravity model. To further investigate the drivers of the “dual carbon” policy diffusion, we conducted a simulation of network evolution under the two policy diffusion mechanisms of coercion and learning. Our findings indicate that the “dual carbon” policy exhibits a network diffusion effect, with cost-effectiveness serve as a critical factor influencing its spread. And the China's low carbon price lacks incentives for reduction, carbon pricing needs to be increased and differentiated emission reduction incentives, penalties for over-emission, and carbon quotas based on the type of decoupling of the city need to be introduced to facilitate the diffusion of policies. Additionally, through counterfactual scenario analysis and an exploration of network structural characteristics, we highlighted the vital role of cities with high betweenness centrality within urban agglomerations in facilitating efficient policy diffusion. This study also offers a process for selecting pilot cities, providing a valuable reference for future policy initiatives.

Research on the Evolution of the Spatial Association Network Structure and Driving Factors of China’s Agricultural Green Development

Against the backdrop of global environmental challenges and sustainable development goals, this paper pioneers the application of social network analysis to the study of spatial associations in China’s agricultural green development. It not only enhances the understanding of the spatial interconnectivity and network structural characteristics of agricultural green developments, but also captures the complex dependencies and interactions among provinces through a network lens, offering a fresh perspective on regional agricultural cooperation and competition. The study reveals: (1) The spatial network of China’s agricultural green development displays strong overall connectivity and enhanced stability, with regional green development trends becoming increasingly interlinked and interdependent. (2) The network exhibits a clear hierarchical and core-periphery structure which, over time, shows signs of diminishing, indicating a narrowing of developmental disparities among regions. (3) Significant shifts in the roles and positions of provinces within the network occur due to the relocation of industrial focal points and adjustments in development strategies, highlighting the complexity of dynamic changes among regions. (4) The spatial association network can be divided into four main clusters: Net spillover block, Bidirectional spillover block, Net beneficial block, and Broker block, with significant gradient characteristics in the relationships between these clusters, suggesting directional and differential flows and exchanges of resources and information among regions. (5) Geographic proximity, economic development level, informatization, and agricultural technological advancement significantly influenced the development and structural evolution of the network.

Network constraint and online group innovation: a dual perspective of inter-community group network and intra-community group network

PurposeThis study aims to explore the different effects of inter-community group networks and intra-community group networks on group innovation.Design/methodology/approachThe authors used a pooled panel dataset of 12,111 self-organizing innovation groups in 463 game product creative workshop communities from Steam support to test the hypothesis. The pooled ordinary least squares (OLS) model is used for analyzing the data.FindingsThe results show that network constraint is negatively associated with the innovation performance of online groups. The average path length of the inter-community group network negatively moderates the relationship between network constraint and group innovation, while the average path length of the intra-community group network positively moderates the relationship between network constraint and group innovation. In addition, both the network density of inter-community group networks and intra-community group networks can negatively moderate the negative relationship between network constraint and group innovation.Originality/valueThe findings of this study suggest that network structural characteristics of inter-community networks and intra-community networks have different effects on online groups’ product innovation, and therefore, group members should consider their inter- and intra-community connections when choosing other groups to form a collaborative innovation relationship.

Diagnostic discrimination of social network indicators in alcohol use disorder: Initial examination using high-resolution and brief assessments.

Social network analysis (SNA) characterizes the structure and composition of a person's social relationships. Network features have been associated with alcohol consumption in observational studies, primarily of university undergraduates. No studies have investigated whether indicators from a person's social network can accurately identify the presence of alcohol use disorder (AUD), offering an indirect strategy for identifying AUD. Two cross-sectional case-control designs examined the clinical utility of social network indicators for identifying individuals with AUD (cases) versus demographically matched drinkers without AUD (controls). Study 1 (N = 174) used high-resolution egocentric SNA assessment, whereas Study 2 (N = 189) used a brief assessment. In Study 1, significant differences between AUD+ participants and controls were present for network alcohol severity (i.e., heavy drinking days; d = 1.23) and frequency (d = 0.35), but not network structural features. Network alcohol severity exhibited very good classification of AUD+ individuals versus controls (area under the curve [AUC] = 0.80), whereas network frequency did not (AUC = 0.61). In Study 2, significant differences were present for network alcohol severity (d = 1.02), quantity (d = 0.74), and frequency (d = 0.43), and severity exhibited good differentiation (AUC = 0.76). Social network indicators of alcohol involvement robustly differentiated AUD+ individuals from matched controls, and the brief assessment performed almost as well as the high-resolution assessment. These findings provide proof-of-concept for severity-related SNA indicators as promising novel clinical assessments for AUD. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Examining the Role of Structural and Functional Social Network Characteristics in the Context of Chronic Pain: An Ego-centered Network Design

The well-being and functioning of individuals with chronic pain (CP) vary significantly. Social factors, such as social integration, may help explain this differential impact. Specifically, structural (network size, density) as well as functional (perceived social support, conflict) social network characteristics may play a role. However, it is not yet clear whether and how these variables are associated with each other. Objectives were to examine 1) both social network characteristics in individuals with primary and secondary CP, 2) the association between structural network characteristics and mental distress and functioning/participation in daily life, and 3) whether the network’s functionality mediated the association between structural network characteristics and mental distress, respectively, functioning/participation in daily life. Using an online ego-centered social network tool, cross-sectional data were collected from 303 individuals with CP (81.85% women). No significant differences between individuals with fibromyalgia versus secondary CP were found regarding network size and density. In contrast, ANCOVA models showed lower levels of perceived social support and higher levels of conflict in primary (vs secondary) CP. Structural equation models showed that 1) larger network size indirectly predicted lower mental distress via lower levels of conflict; 2) higher network density increased mental distress via the increase of conflict levels. Network size or density did not (in)directly predict functioning/participation in daily life. The findings highlight that the role of conflict, in addition to support, should not be underestimated as a mediator for mental well-being. Research on explanatory mechanisms for associations between the network’s structure, functionality, and well-being is warranted. PerspectiveThis paper presents results on associations between structural (network size, density) and functional (social support, conflict) social network characteristics and well-being in the context of CP by making use of an ego-centered network design. Results suggest an indirect association between structural network characteristics and individuals with CP their mental well-being.

Intelligent recommendation system for College English courses based on graph convolutional networks

With the rapid development of international communication, the number of English courses has shown an explosive growth trend, which has caused a serious problem of information overload, resulting in poor teaching performance of recommended English courses. To solve this problem, this paper proposes a graph convolutional neural network model based on College English course texts, students’ major, English foundation and network structure characteristics. First, by analyzing the relevant data of College English courses and combining with graph neural network, an English course recommendation algorithm model based on the College English learning strategy of proximity comparison is proposed. Then, the College English texts are taken as feature input, and multi-layer graph convolutional neural network is used to process the above graph neural network structure. Attention mechanism is introduced to enhance the representation of graph features in College English skills. Finally, multi-layer attention model is used to process the courses that users have learned, and intelligent course recommendation is made by combining the multi-layer attention modeling of College English skills. The experimental data show that the proposed method achieves the best performance compared with the commonly used College English course recommendation method.

How Network Structure Shapes Languages: Disentangling the Factors Driving Variation in Communicative Agents.

Languages show substantial variability between their speakers, but it is currently unclear how the structure of the communicative network contributes to the patterning of this variability. While previous studies have highlighted the role of network structure in language change, the specific aspects of network structure that shape language variability remain largely unknown. To address this gap, we developed a Bayesian agent-based model of language evolution, contrasting between two distinct scenarios: language change and language emergence. By isolating the relative effects of specific global network metrics across thousands of simulations, we show that global characteristics of network structure play a critical role in shaping interindividual variation in language, while intraindividual variation is relatively unaffected. We effectively challenge the long-held belief that size and density are the main network structural factors influencing language variation, and show that path length and clustering coefficient are the main factors driving interindividual variation. In particular, we show that variation is more likely to occur in populations where individuals are not well-connected to each other. Additionally, variation is more likely to emerge in populations that are structured in small communities. Our study provides potentially important insights into the theoretical mechanisms underlying language variation.

Unveiling the Impact of Communication Network on Engineering Project Team Performance: The Interplay of Centralization and Tie Strength.

Recent research has focused on the impact of communication networks on the performance of construction project teams, attempting empirical exploration from various social network analysis perspectives. However, there is still a significant gap in understanding the variations in performance and the mechanisms for teams using different communication networks. Drawing from organizational learning theory and social network theory, this study, based on the Input-Mediator-Output (IMO) model, explores the effects of the interaction between centralization and tie strength in communication networks on team performance, as well as the mediating mechanisms of knowledge sharing behavior and team resilience performance in engineering project teams. Drawing on classic group communication experiment, we design an online communication and collaboration platform to simulate the execution of a construction engineering project. Finally, data was collected through the communication experiment with 720 participants, and hypotheses were tested using ANOVA and PROCESS. The results indicate that under conditions of weak tie strength, centralized communication networks yield higher performance. Conversely, under conditions of strong tie strength, decentralized communication networks demonstrate superior performance. Furthermore, this study also verifies the mediating role of knowledge sharing behavior and team resilience performance when tie strength is strong. This study focuses on engineering project team, exploring the evolutionary development of knowledge sharing behavior and team resilience performance from the perspective of the interaction of communication network structural characteristics, as well as the paths to enhancing team performance. Our research results highlight the interactive effects of structural indicators and relational indicators of communication networks, revealing the mechanism by which the structure of communication networks impacts team performance. Additionally, from the perspectives of forming and timely adjusting team communication models, and motivating and supporting employee communication behavior, our study provides practical insights for project managers and relevant administrators.

Effective graph-neural-network based models for discovering Structural Hole Spanners in large-scale and diverse networks

A Structural Hole Spanner (SHS) is a set of nodes in a network that act as a bridge among different otherwise disconnected communities. While several solutions exist for SHS discovery, they often suffer from high runtime, particularly in large-scale networks. Additionally, discovering SHSs across diverse networks poses another challenge, as the traditional one-model-fits-all approach fails to capture inter-graph differences, especially in diverse networks. Therefore, there is an urgent need of developing effective solutions for discovering SHSs in large-scale and diverse networks. Inspired by the recent advancement of graph neural network approaches on various graph problems, we propose graph neural network-based models to discover SHS nodes in large-scale and diverse networks. Our approach transform the problem into a learning problem and propose an efficient model GraphSHS, that exploits both the network structure and node features to discover SHS nodes in large scale networks, endeavouring to lessen the computational cost while maintaining high accuracy. To effectively discover SHSs across diverse networks, we propose another model Meta-GraphSHS based on meta-learning that learns generalizable knowledge from diverse training graphs (instead of directly learning the model) and utilizes the learned knowledge to create a customized model to identify SHSs in each new graph. Theoretically, we demonstrate that the depth of the proposed graph neural network model should be at least Ω(n/logn) to accurately calculate the SHSs discovery problem. We evaluate the performance of the proposed models through extensive experiments on synthetic and real-world datasets. Our experimental results show that GraphSHS discovers SHSs with high accuracy and is at least 167.1 times faster than the comparative methods on large-scale real-world datasets. Furthermore, Meta-GraphSHS effectively discovers SHSs across diverse synthetic networks with an accuracy of 96.2%.

Personalized Driver Gene Prediction Using Graph Convolutional Networks with Conditional Random Fields.

Cancer is a complex and evolutionary disease mainly driven by the accumulation of genetic variations in genes. Identifying cancer driver genes is important. However, most related studies have focused on the population level. Cancer is a disease with high heterogeneity. Thus, the discovery of driver genes at the individual level is becoming more valuable but is a great challenge. Although there have been some computational methods proposed to tackle this challenge, few can cover all patient samples well, and there is still room for performance improvement. In this study, to identify individual-level driver genes more efficiently, we propose the PDGCN method. PDGCN integrates multiple types of data features, including mutation, expression, methylation, copy number data, and system-level gene features, along with network structural features extracted using Node2vec in order to construct a sample-gene interaction network. Prediction is performed using a graphical convolutional neural network model with a conditional random field layer, which is able to better combine the network structural features with biological attribute features. Experiments on the ACC (Adrenocortical Cancer) and KICH (Kidney Chromophobe) datasets from TCGA (The Cancer Genome Atlas) demonstrated that the method performs better compared to other similar methods. It can identify not only frequently mutated driver genes, but also rare candidate driver genes and novel biomarker genes. The results of the survival and enrichment analyses of these detected genes demonstrate that the method can identify important driver genes at the individual level.

Assessment of network structure characteristics and factors of corporate flows in Guangdong Province.

With the rapid development of the world city network, the traditional location theory has gradually been disproven, and the advantages of the flow space over the traditional vertical organizational structure are gradually being revealed. Therefore, from corporate branch networks and corporate investment networks, 21 cities in urban agglomerations of Guangdong are taken as case studies for this paper. Furthermore, in this paper, 5 representative types of corporate contact data (catering service, financial service, life service, sports and leisure and accommodation service) are selected, the social network analysis (SNA) method is used to quantitatively analyze the network structure characteristics of urban agglomerations, and a spatial interaction model is constructed to explore the factors influencing. The results indicate that secondary networks have developed in Guangdong. The financial service network is the most complex, followed by the life services, sports and leisure and catering networks. The accommodation service network structure is the simplest. Among all kinds of networks, Guangzhou and Shenzhen have the highest status. The catering and accommodation corporations in Yangjiang in the west have a relatively major external development. Shantou in the east has many branches of various types, while most of the capital exchange in the region is concentrated in Heyuan and Qingyuan in the north. The coefficients of geographical proximity and the urban development level play a significant role in promoting the development of networks. However, administrative capacity limits the attractiveness of origin cities to a certain extent.

Multiple criteria decision modeling in social networks: An application to identify opinion leaders in online public opinion events

For online public opinion events, the misconduct of opinion leaders can easily lead to the spread of negative public opinion, which in turn can adversely affect real society. In order to minimize this adverse impact to the greatest extent, this paper uses social network analysis to explore the attributes of individuals, features of network structure, and social relationships between decision makers (DMs). It constructs the structural system of evaluation criteria and a multiple criteria decision model to comprehensively and reasonably identify opinion leaders. It additionally considers the importance of criteria and the interaction among them. The hierarchical structure is combined with the evaluation level to construct a precise structural system of criteria. To handle the interaction, the Choquet integral is applied as the preference model. Based on the disaggregation principle, a generalized hierarchical level dependent Choquet integral is developed to reproduce additional preference information provided by decision analysts (DAs) and aggregate evaluation values of DMs on multiple interacting criteria. Then, the relevant indices of this integral and the preference information are applied to robust ordinal regression to mine rich preference relations among DMs. Furthermore, the hierarchical level dependent Choquet integral-driven multiple criteria decision model is proposed to rank DMs and identify opinion leaders. Finally, a numerical example and two real datasets validate the effectiveness and advantages of the proposed decision model.

The spatial correlation network structure and its formation mechanism of urban high-quality economic development: a comparative analysis from the Yangtze River Economic Belt and the Yellow River Basin in China.

In the context of regional integration, it is more than crucial to compare and analyze the spatial correlation network structure and formation mechanism of high-quality economic development in the Yangtze River Economic Belt and the Yellow River Basin urban cities as an attempt to strengthen collaborative work on high-quality economic development in both river basins. The paper measured high-quality economic development of the Yangtze River Economic Belt and the Yellow River Basin from 2010 to 2021. Then, it employed social network analysis and the QAP method to study the network structure's characteristics and formation mechanism. The conclusion of the research illustrates a few points clearly that first, the high-quality economic development of the two rivers presents a complex and multithreaded network structure. Although the network structure is hold at a comparatively stable state, the correlation degree needs improvement. Second, cities such as Chongqing, Wuhan, Hefei, Nanjing, Hangzhou, Shanghai, and Changsha and cities like Zhengzhou, Xi'an, Luoyang, Yulin, Hulunbuir, Ordos, and Nanyang are at the very central as well as central position of the network. The spatial correlation networks of the Yangtze River Economic Belt and the Yellow River Basin can be divided into four plates: "agent plate," "main outflow plate," "bidirectional spillover plate," and "main inflow plate." Third, reverse geographical distance and differences in the digital economy attach great significance to the spatial correlation networks of the two basins. The difference in urbanization level makes significant impacts only on the spatial correlation network of the Yangtze River Economic Belt, while the difference in environmental regulation and material capital accumulation only significantly influences the spatial correlation network of the Yellow River Basin.

The interaction of information diffusion and epidemic transmission in time-varying multiplex networks with simplicial complexes and asymmetric activity levels

Information diffusion among individuals occurs in various ways, mainly involving pairwise and higher-order interactions, and their coexistence can be characterized by simplicial complexes. This paper introduces a novel two-layer model that investigates coupled information-epidemic propagation. Specifically, the upper layer represents the virtual layer that depicts information diffusion, where the interaction process among individuals is not only limited to pairwise interactions but also influenced by higher-order interactions. The lower layer denotes the physical contact layer to portray epidemic transmission, where the interaction process among individuals is only considered in pairwise interactions. In particular, the emergence of asymmetric activity levels in two-layer networks reshapes the propagation mechanism. We then employ the micro-Marko chain approach (MMCA) to establish the probabilistic transfer equation for each state, deduce the epidemic outbreak threshold, and investigate the equilibrium and stability of the proposed model. Furthermore, we perform extensive Monte Carlo (MC) simulations to validate the theoretical predictions. The results demonstrate that the higher-order interaction generates synergistic reinforcement mechanisms that both facilitate information diffusion and inhibit epidemic transmission. Moreover, this study suggests that the activity level of individuals at the physical contact level has a greater impact on epidemic transmission. In addition, we utilize two different networks to explore the influence of network structural features on the transmission and scale of epidemics.

Multi-channel high-order network representation learning research.

The existing network representation learning algorithms mainly model the relationship between network nodes based on the structural features of the network, or use text features, hierarchical features and other external attributes to realize the network joint representation learning. Capturing global features of the network allows the obtained node vectors to retain more comprehensive feature information during training, thereby enhancing the quality of embeddings. In order to preserve the global structural features of the network in the training results, we employed a multi-channel learning approach to perform high-order feature modeling on the network. We proposed a novel algorithm for multi-channel high-order network representation learning, referred to as the Multi-Channel High-Order Network Representation (MHNR) algorithm. This algorithm initially constructs high-order network features from the original network structure, thereby transforming the single-channel network representation learning process into a multi-channel high-order network representation learning process. Then, for each single-channel network representation learning process, the novel graph assimilation mechanism is introduced in the algorithm, so as to realize the high-order network structure modeling mechanism in the single-channel network representation learning. Finally, the algorithm integrates the multi-channel and single-channel mechanism of high-order network structure joint modeling, realizing the efficient use of network structure features and sufficient modeling. Experimental results show that the node classification performance of the proposed MHNR algorithm reaches a good order on Citeseer, Cora, and DBLP data, and its node classification performance is better than that of the comparison algorithm used in this paper. In addition, when the vector length is optimized, the average classification accuracy of nodes of the proposed algorithm is up to 12.24% higher than that of the DeepWalk algorithm. Therefore, the node classification performance of the proposed algorithm can reach the current optimal order only based on the structural features of the network under the condition of no external feature supplementary modeling.

An in-depth analysis of green innovation efficiency: New evidence based on club convergence and spatial correlation network

Green innovation is the cornerstone of global economic transformation. Based on the green innovation efficiency calculated under the two-stage innovation process, this paper discusses its convergence and network structure characteristics by applying the club convergence model and social network analysis method. The quadratic assignment procedure approach is employed to analyze the influencing factors. The results reveal that: (1) this paper finds β convergence but not convergence within subgroups based on regional classification. (2) Regarding network correction characteristics, the correlation ties and network density of the green innovation efficiency network demonstrate a downward trend, while the network hierarchy characteristics are still apparent. (3) The regional green innovation efficiency cluster performs different roles, as the core provinces become the beneficiaries of green innovation resources. (4) The differences in regional development and the clustering of block models are unfavorable to the formation of green innovation efficiency networks, while the differences in geographical proximity, green finance, resource environmental carrying capacity, and green innovation R&D efficiency foster the development of the spatial network. This paper contributes to promoting coordinated regional green innovation efficiency development.

Complex networks with complex weights.

In many studies, it is common to use binary (i.e., unweighted) edges to examine networks of entities that are either adjacent or not adjacent. Researchers have generalized such binary networks to incorporate edge weights, which allow one to encode node-node interactions with heterogeneous intensities or frequencies (e.g., in transportation networks, supply chains, and social networks). Most such studies have considered real-valued weights, despite the fact that networks with complex weights arise in fields as diverse as quantum information, quantum chemistry, electrodynamics, rheology, and machine learning. Many of the standard network-science approaches in the study of classical systems rely on the real-valued nature of edge weights, so it is necessary to generalize them if one seeks to use them to analyze networks with complex edge weights. In this paper, we examine how standard network-analysis methods fail to capture structural features of networks with complex edge weights. We then generalize several network measures to the complex domain and show that random-walk centralities provide a useful approach to examine node importances in networks with complex weights.

Assessment and solutions for vulnerability of urban rail transit network based on complex network theory: A case study of Chongqing

As a typical complex network system, the operating environment of rail transit network (RTN) is complex and demanding. This study aims to accurate assess the weaknesses and vulnerability of RTN, which is crucial for ensuring its smooth operation. Taking Chongqing Rail Transit (CRT) as an example, this study developed a network topology model using the spatial L method and analyzed the network structure characteristics, along with the importance of key nodes under different indicators, based on complex network theory. Additionally, this study analyzed the geographical spatial distribution characteristics of nodes based on the topography and urban spatial structure of Chongqing. Then, this study classified the nodes in the RTN according to basic topological indicators, namely degree, betweenness centrality, network efficiency, and passenger flow volume (PFV). The results indicated six cluster of nodes, reflecting the variability in node vulnerability concerning overall influence (providing alternative paths, reducing path length), regional aggregation capacity, and transportation capacity. Finally, this study proposed targeted management strategies for different clusters of nodes and their respective geographical locations, providing necessary references for rational planning, safety protection, and sustainable construction of RTN.