Network Structure Characteristics Research Articles

Cross-Social-Network User Identification Based on Bidirectional GCN and MNF-UI Models

Due to the distinct functionalities of various social network platforms, users often register accounts on different platforms, posing significant challenges for unified user management. However, current multi-social-network user identification algorithms heavily rely on user attributes and cannot perform user identification across multiple social networks. To address these issues, this paper proposes two identity recognition models. The first model is a cross-social-network user identification model based on bidirectional GCN. It calculates user intimacy using the Jaccard similarity coefficient and constructs an adjacency matrix to accurately represent user relationships in the social network. It then extracts cross-social-network user information to accomplish user identification tasks. The second model is the multi-network feature user identification (MNF-UI) model, which introduces the concept of network feature vectors. It effectively maps the structural features of different social networks and performs user identification based on the common features of seed nodes in the cross-network environment. Experimental results demonstrate that the bidirectional GCN model significantly outperforms baseline algorithms in cross-social-network user identification tasks. The MNF-UI (multi-network feature user identification) model can operate in situations with two or more networks with inconsistent structures, resulting in improved identification accuracy. These two user identification algorithms provide technical and theoretical support for in-depth research on social network information integration and network security maintenance.

Trade network dynamics in a globalized environment and on the edge of crises

At the edges of crises, the roles of trade networks and supply chains are evident. Global trade networks aggregate all legal trade activities. Thus, researchers believe that network structure changes reflect phenomena such as crises, globalization, deglobalization, or even technological development, including those related to green technologies. Although trade network analysis has been widely researched, researchers have focused mainly on one or a few products or product groups. While the impact of crises and technological changes in the case of individual products has been demonstrated in some studies, less attention has been given to structural pattern changes in trade on these products. To understand how a shock, crisis, or technological change in one product may affect the trade network of another product, the patterns and relationships of node- and network-level indicators across products need to be identified. In this study, the dynamics and interdependencies of trade networks are analyzed using a combination of network indicators, cluster analysis and causality analysis. Variations and implications of the temporal patterns and causal relationships of trade network indicators are revealed for different products and product groups. Researchers have argued that the deterioration of structural indicators exacerbates crises and amplifies their consequences. A causality analysis shows that disruptions in one product’s supply chain quickly affect the trade of other products, altering the structural characteristics of trade networks. The proposed temporal pattern analysis shows that while the 2008 financial crisis did not cause structural changes in the product trade network, indicators improved before, during, and after the crisis. Nevertheless, before the pandemic, there was a noticeable deterioration in almost all structural indicators, signaling more deglobalization. This study contributes to a comprehensive understanding of trade networks, equipping decision-makers with the knowledge to promote the resilience and sustainability of these networks in an evolving global landscape.

Network Structure Characteristics and Influencing Factors of Urban Agglomerations in China under Impact of COVID-19

In the context of COVID-19, the efforts undertaken for epidemic control have imposed limitations on the multifaceted development of China. This manuscript utilizes Baidu migration data from 2019 to 2023 to classify the current developmental status of urban agglomerations (UAs) in China. The explication of network structure is achieved through the computation of metrics that capture network structural connectivity and hierarchical attributes. Additionally, an inquiry into the spatio-temporal differentiation of the UAs’ network structure is carried out, encompassing three phases: before COVID-19, the normalization stage of COVID-19, and after COVID-19. Furthermore, Quantitative Analysis of Patterns (QAP) is employed to assess the impact of diverse influencing factors. The analysis yields several key findings: ① The impact of COVID-19 on the network structure of China’s UAs manifests in two discernible stages—initial impact disruption and subsequent recovery and reconstruction. ② The exploration of pertinent influencing factors during the primary stage of UA development is impeded. ③ The growth stage and the UAs with a high level of development exhibit have a closely intertwined relationship, fostering a more rational hierarchical structure and demonstrating an enhanced capacity for swift recovery. ④ It is discerned that economic development level, medical facility standards, transportation infrastructure capacity, spatial proximity, and innovation accessibility exert a discernible influence on the network structure of UAs. Importantly, the extent of impact varies across different periods and types of UAs.

AI-2 quorum sensing signal disrupts coral symbiotic homeostasis and induces host bleaching

Symbiotic microorganisms play critical ecophysiological roles that facilitate the maintenance of coral health. Currently, information on the gene and protein pathways contributing to bleaching responses is lacking, including the role of autoinducers. Although the autoinducer AI-1 is well understood, information on AI-2 is insufficient. Here, we observed a 3.7–4.0 times higher abundance of the AI-2 synthesis gene luxS in bleached individuals relative to their healthy counterparts among reef-building coral samples from the natural environment. Laboratory tests further revealed that AI-2 contributed significantly to an increase in coral bleaching, altered the ratio of potential probiotic and pathogenic bacteria, and suppressed the antiviral activity of specific pathogenic bacteria while enhancing their functional potential, such as energy metabolism, chemotaxis, biofilm formation and virulence release. Structural equation modeling indicated that AI-2 influences the microbial composition, network structure, and pathogenic features, which collectively contribute to the coral bleaching status. Collectively, our results offer novel potential strategies for coral conservation based on a signal manipulation approach.

Evolution characteristics and influencing factors of information network in Guangdong-Hong Kong-Macao Greater Bay Area.

In the context of the digital information era, the impact of "The Internet Plus," "Big Data," and other technologies on urban social development has been far beyond any preceding era, under the influence of information technology, urban agglomeration space exhibits a new layout. Based on the search engine data of eleven cities in the Guangdong-Hong Kong-Macao Greater Bay Area from 2012 to 2021, this research constructs the inter-city information network strength linkage matrix to examine the evolution characteristics of city network structure and its driving causes. The results reveal that (1) the overall information linkage strength exhibits a pattern of steadily growing the radiating effect from the leading cities of Guangdong, Shenzhen, and Hong Kong to the surrounding cities, and a closer and more balanced information linkage network is gradually built. (2) Guangzhou-Shenzhen-Hong Kong-Guangdong-Hong Kong-Macao Greater Bay Area information linkage absolute control advantage, four cities Foshan, Dongguan, Zhuhai, Macao regional hub position steadily highlighted. The entire information connection network of the urban agglomerations tends to be flat and polycentric at the same time. (3) The regional core-edge hierarchy is well established, with the four cities of Guangzhou, Dongguan, Shenzhen, and Hong Kong creating a northwest-southeast orientation. The core metropolis regions of Guangdong, Hong Kong, and Macao in the Greater Bay Area increasingly exert a radiation spreading effect to the northeast and southwest. (4) The urban economy, transportation distance, and information infrastructure have substantial effects on the information connection intensity network of urban clusters.

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.

Gabor-net with multi-scale hierarchical fusion of features for fundus retinal blood vessel segmentation

This paper proposes a fundus retinal blood vessel segmentation model based on a deep convolutional network structure and biological visual feature extraction mechanism. It aims to solve the multi-scale problem of blood vessels in the fundus retinal blood vessel segmentation task in the field of medical image processing on the basis of increasing the biological interpretability of the model. First, the subject feature information of the retinal blood vessel image is obtained by using the non-subsampled Residual Bolck convolution main channel. Secondly, combined with the study of biological vision mechanisms, an information processing model of the Retina-Exogenius-Primary visual cortex (V1) ventral visual pathway was established. Gabor functions of different scales are used to simulate the structure of different levels of the visual pathway, and the scale information at different levels is integrated into the corresponding hierarchical stages of the convolutional main pathway network to enrich the information of small blood vessels and enhance the semantic information of the overall blood vessels. Finally, considering the imbalance of the ratio of vessel and nonvessel pixels, an adaptive optimization scheme using hybrid loss function weights is proposed to enhance the priority of blood vessel pixels in the calculation of the loss function. According to the experimental results on the STARE, DRIVE and CHASE_DB1 data sets, the model still achieves superior performance evaluation indicators overall compared with the existing optimal methods in the fundus retinal blood vessel segmentation task. This research is of great significance to the field of medical image processing and can provide more accurate auxiliary diagnostic information for clinical diagnosis and treatment.

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.