Social Network Analysis Models Research Articles

Assessing territorial space conflicts in the coastal zone of Wenzhou, China: A land-sea interaction perspective

Territorial space conflicts (TSCs) in coastal zones stem from the intricate interaction between the land-sea dual system, significantly impacting the sustainable development of these areas. To accurately identify TSCs, our study proposed a theoretical framework based on a land-sea interaction perspective. We also assessed TSCs using the territorial space conflicts index and a social network analysis model. We demonstrated the proposed spatial conflicts assessment methodology through a case study of Wenzhou, a typical city on the east coast of China. Our results indicate that the distribution of TSCs gradually decreased from the coastal zone to the inland zone, with significant variation in the distribution of different conflict types across different zones. The findings also reveal that territorial space use had diverse impacts on the space conflict network, making it urgent to take targeted measures. In the future, it is crucial to comprehensively consider the overall pattern and distribution characteristics of current TSCs, as well as the spatial spillover effect of the overall network, to develop targeted coping strategies and regulation mechanisms that promote the integration and high-quality development of coastal territorial space systems. To maintain a sustainable coastal zone process, we proposed a set of optimization paths for alleviating TSCs and promoting the coordinated development of land and sea regions in China based on our study.

Exploring the Effective Path of Cultivating College Students’ English Intercultural Communication Competence Based on the Social Network Analysis Model

Abstract This paper introduces the BOPPPS teaching model integrated with flipped classrooms into the intercultural communication classroom by decomposing the BOPPPS teaching module and analyzing the differences with traditional teaching methods by combining social network analysis methods. The basic teaching model framework was built, and an effective blended teaching model was created by utilizing the third-order nonlinear expression of social networks introduced into the online platform. A 10-week teaching model experiment was conducted for two classes to explore English major students in a university. The overall average score of the experimental class improved by 23.6 points to 86.02, and the score improvement was significantly higher than that of the control class, proving that the hybrid teaching model is an effective way to cultivate talents for universities.

Study on the Correlation Characteristics between Scenic Byway Network Accessibility and Self-Driving Tourism Spatial Behavior in Western Sichuan

The scenic byways in Western Sichuan are some of the most popular self-driving tourism destinations in China. However, the current network of scenic byways in the region is not well-coordinated with the level of regional tourism development. This paper, based on travel digital footprints, uses methods such as spatial design network analysis, GIS spatial analysis, social network analysis models, and spatial econometric models to analyze the accessibility and self-driving tourism spatial behavior characteristics in Western Sichuan. The main research results are as follows: (1) the accessibility level of scenic byways in Western Sichuan exhibits significant spatial variation, with the majority of areas demonstrating moderate to poor accessibility; (2) the network structure of self-driving tourism spatial behavior displays characteristics of low overall network density, but with a high clustering coefficient and relatively short average path length, indicating a significant small-world phenomenon. All network node indicators exhibit significant heterogeneity, with the core nodes displaying clear clustering characteristics; (3) the accessibility of scenic byways and self-driving tourism spatial behavior exhibit significant spatial spillover effects. This study analyzes the relationship between the accessibility of scenic byways and self-driving tourism spatial behavior in Western Sichuan, providing valuable insights for the planning and construction of scenic byways and the development of self-driving tour routes.

A social network analysis model approach to understand tuberculosis transmission in remote rural Madagascar

BackgroundQuality surveillance data used to build tuberculosis (TB) transmission models are frequently unavailable and may overlook community intrinsic dynamics that impact TB transmission. Social network analysis (SNA) generates data on hyperlocal social-demographic structures that contribute to disease transmission.MethodsWe collected social contact data in five villages and built SNA-informed village-specific stochastic TB transmission models in remote Madagascar. A name-generator approach was used to elicit individual contact networks. Recruitment included confirmed TB patients, followed by snowball sampling of named contacts. Egocentric network data were aggregated into village-level networks. Network- and individual-level characteristics determining contact formation and structure were identified by fitting an exponential random graph model (ERGM), which formed the basis of the contact structure and model dynamics. Models were calibrated and used to evaluate WHO-recommended interventions and community resiliency to foreign TB introduction.ResultsInter- and intra-village SNA showed variable degrees of interconnectivity, with transitivity (individual clustering) values of 0.16, 0.29, and 0.43. Active case finding and treatment yielded 67%–79% reduction in active TB disease prevalence and a 75% reduction in TB mortality in all village networks. Following hypothetical TB elimination and without specific interventions, networks A and B showed resilience to both active and latent TB reintroduction, while Network C, the village network with the highest transitivity, lacked resiliency to reintroduction and generated a TB prevalence of 2% and a TB mortality rate of 7.3% after introduction of one new contagious infection post hypothetical elimination.ConclusionIn remote Madagascar, SNA-informed models suggest that WHO-recommended interventions reduce TB disease (active TB) prevalence and mortality while TB infection (latent TB) burden remains high. Communities’ resiliency to TB introduction decreases as their interconnectivity increases. “Top down” population level TB models would most likely miss this difference between small communities. SNA bridges large-scale population-based and hyper focused community-level TB modeling.

A hybrid DEMATEL and social network analysis model to identify factors affecting learners' satisfaction with MOOCs

Massive Online Open Courses (MOOCs) offer free access to training in various topics in all fields. However, the low percentage of course completion by learners is a significant challenge for these platforms. Previous studies on this challenge have investigated user behavior and concerned topics in discussion forums, but these data are mostly momentary and cannot be used for long-term improvement. Thus, this study aimed to address this gap by analyzing learners' comments to identify the factors affecting user satisfaction and prioritize them to improve MOOC platforms. The purpose was to analyze the feedback and actual experiences of users shared through their comments on MOOC online platforms to explore factors affecting user satisfaction to optimize MOOC platforms. To achieve this, sentiment analysis and topic modeling techniques were applied to the user feedback on courses with popular topics, such as Skills for Data Science Teams and Data-Driven Decision Making, available on Coursera.com. The study used DEMATEL analysis, which uses a relation matrix of factors to rank them based on their interrelationships, and network analysis to prioritize the factors that should be improved to achieve the highest user satisfaction. The effect of the proposed approach was investigated through a case study on a course from Coursera. The findings demonstrate that the suggested method has the potential to assist MOOC platforms in several ways. Firstly, it enables the identification of course strengths and weaknesses. Secondly, it allows for the identification of factors that influence learner satisfaction by analyzing user feedback. Lastly, it aids in prioritizing the factors that should be enhanced to attain optimal user satisfaction, thus leading to overall improvement in the status of the MOOC platform.

A social network analysis approach to estimate export disruption spread in the US during the Covid-19 pandemic: how policy response and industry ties relate

We examine how the Covid-19 pandemic led to the propagation of export disruptions on a state-by-state basis using a social network analysis model. We measure the impact of import disruptions, Covid-related hospitalizations, subsequent policy responses, and structural network effects on economic outcomes. In addition to examining contemporaneous effects, we include lagged policy response variables to determine their effect on disruption recovery trends. Findings suggest that disruptions cluster along shared industry connections. The results are consistent with previous work that shows that non-pharmaceutical policy interventions had limited contemporaneous and medium-term effects on trade flows.

Provincial Inclusive Green Growth Efficiency in China: Spatial Correlation Network Investigation and Its Influence Factors

Inclusive green growth efficiency (IGGE) analysis is an effective tool for improving coordinated economic, social, and environmental development. This study incorporated the game cross-efficiency DEA to measure the IGGE of 30 provinces in China. Then, the modified spatial gravity model and social network analysis model were applied to construct and analyze the spatial correlation network structure of the IGGE. The quadratic assignment procedure was used to mine the influencing factors that affect the formation and evolution of the spatial correlation network of the IGGE. The results are as follows. (1) During the study period, there were significant differences in the IGGE among the 31 provinces, among which the eastern provinces were higher than the central and western provinces. (2) The spatial correlation of the IGGE presented a complex and multi-threaded network structure, indicating that the IGGE has a noticeable cross-regional spillover effect. Beijing, Tianjin, Zhejiang, Shanghai, Jiangsu, and Guangdong played the role of the “net spillover” block. Qinghai, Guizhou, Guangxi, and the surrounding provinces played the role of the “primary beneficial”. The Yangtze delta and Pearl River Delta economic zone (primarily including Shanghai and Guangdong) acted as a “bridge” to the Yunnan–Guizhou region and the surrounding provinces. (3) The spatial adjacency, degree of openness, economic development, and environmental governance were the prominent factors influencing the formation and evolution of the IGGE spatial correlation network. This work provides an example of constructing an IGGE correlation network while considering various factors, such as the economy, population, and distance. It also could help policymakers clarify the IGGE spatial correlation pattern and the provinces’ roles and potential for IGGE synergic improvement.

Analysis of Influencing Factors of Urban Community Function Loss in China under Flood Disaster Based on Social Network Analysis Model.

The increasing frequency of floods is causing an increasing impact on urban communities. To identify the key influencing factors of functional loss in Chinese urban communities under floods, this paper explored the influencing factors and factor combinations through a social network analysis approach using the 265 cases of urban communities in China affected by floods collected from 2017–2021 as research data. The key influencing factors and factor combinations were identified comprehensively using multiple indicator analyses such as core-periphery structure, node centrality, and factor pairing. The analysis results showed that “road disruption”, “housing inundation”, and “power interruption” are the three most critical factors affecting the functional loss of urban communities in China under floods, followed by “residents trapped”, “enterprises flooded”, and “silt accumulation”. In addition, “road disruption–housing inundation”, “housing inundation–residents trapped”, and “road disruption–residents trapped” are the most common combinations of influencing factors.

Stakeholder-associated impact factors of building energy performance gap and their intersections: A social network analysis

The building energy performance gap is a critical obstacle to emissions reduction and energy conservation. Previous research mainly focused on identifying the factors contributing to the building energy performance gap (BEPG). Such studies have tended toward an inward-looking perspective that treats impact factors in isolation from one another. In reality, most factors are interrelated and associated with energy-related stakeholders. To address this research gap, this research developed a social network analysis model to investigate the underlying network of stakeholder-related influencing factors. According to nodal degree, status centrality, betweenness centrality, and brokerage roles, 21 factors and 14 links that substantially influence other factors directly or indirectly are identified. Six challenges, including “Challenges to knowledge and experience,” “Challenges in determining who is responsible for BEPG among stakeholders,” “Cost challenges,” “Poor quality challenges,” “Challenges to building operation management,” and “Information integrity challenges,” are further identified. Moreover, six strategies that are useful in controlling and eliminating influencing factors and interactions are proposed. The results show that, after the proposed strategies were conducted, the density and cohesion of the network are reduced by 48.28% and 11.62%, respectively, and the top ten betweenness centrality values for the nodes and links are reduced by at least 30%. This research is expected to aid not only researchers in better understanding the intricate interrelationships among the factors influencing BEPG but also practitioners in effectively controlling the influencing factors of BEPG.

A Robust Inference Method for Decision-Making in Networks

Social network data collected from digital sources is increasingly being used to gain insights into human behavior. However, while these observable networks constitute an empirical ground truth, the individuals within the network can perceive the network’s structure differently—and they often act on these perceptions. As such, we argue that there is a distinct gap between the data used to model behaviors in a network, and the data internalized by people when they actually engage in behaviors. We find that statistical analyses of observable network structure do not consistently take these discrepancies into account, and this omission may lead to inaccurate inferences about hypothesized network mechanisms. To remedy this issue, we apply techniques of robust optimization to statistical models for social network analysis. Using robust maximum likelihood, we derive an estimation technique that immunizes inference to errors such as false positives and false negatives, without knowing a priori the source or realized magnitude of the error. We demonstrate the efficacy of our methodology on real social network datasets and simulated data. Our contributions extend beyond the social network context, as perception gaps may exist in many other economic contexts.

Spillover and Re-Spillover in China’s Collaborative Innovation

The spillover effect serves as the basis of regional collaborative innovation. Existing research on innovation spillover focuses on the overall impact of a region's innovation factors on local and other regions' innovation activities. However, re-spillover may occur since the flow of innovation factors between any two regions may influence the innovation in third-party regions. This study quantifies labor flow, capital flow, and institutional learning between regions in China using a gravity model and a social network analysis model, and then applies a spatial econometric model to investigate innovation spillover and re-spillover. The results show that re-spillover can better explain levels of regional innovation. Capital, government support, labour flow, capital flow, and institutional learning have a positive spillover effect on local innovation, while labour flow also has positive spillover effects to other regions.

Comparing the Real-World Performance of Exponential-family Random Graph Models and Latent Order Logistic Models for Social Network Analysis.

Exponential-family Random Graph models (ERGM) are widely used in social network analysis when modelling data on the relations between actors. ERGMs are typically interpreted as a snapshot of a network at a given point in time or in a final state. The recently proposed Latent Order Logistic model (LOLOG) directly allows for a latent network formation process. We assess the real-world performance of these models when applied to typical networks modelled by researchers. Specifically, we model data from an ensemble of articles in the journal Social Networks with published ERGM fits, and compare the ERGM fit to a comparable LOLOG fit. We demonstrate that the LOLOG models are, in general, in qualitative agreement with the ERGM models, and provide at least as good a model fit. In addition they are typically faster and easier to fit to data, without the tendency for degeneracy that plagues ERGMs. Our results support the general use of LOLOG models in circumstances where ERGMs are considered.

Knowledge recombination for emerging technological innovations: The case of green shipping

The paper explores knowledge recombination by analysing how knowledge networks in established technological fields influenced the formation of the emerging field of green shipping in the period 2007–2018. Previous research has demonstrated that embeddedness, proximity, and status are important mechanisms for the evolution of single technological fields. We investigate if these mechanisms also apply across technological fields. By employing dynamic social network analysis models, we find that actors transferred knowledge across technological fields through (re)combination mechanisms, which affected the emergence of the new technological field, but in different ways. While embeddedness and proximity played an important role, status was less important.

Social network analysis based on collaborative innovation performance impact factors of assembled construction industrial park

In order to improve the current situation that innovation in the construction industry is relatively lagging behind, this paper investigates the key influencing factors that affect the collaborative innovation performance of the assembly construction industrial park. Based on the research of previous literature, this paper sorts out the factors affecting the collaborative innovation among innovation subjects in the assembly construction industry, and lists the influencing factors affecting the innovation performance of the industrial park by combining the characteristics of the assembly construction industry, constructs a social network analysis model among the influencing factors through the obtained data, and finally analyzes them by UCINET 6.0 software. The research results show that six factors, namely innovation resource investment, willingness to cooperate, knowledge conversion rate, participants' ability and quality, standardization degree and alliance organization management, are the key factors influencing the collaborative innovation performance of the assembly construction industrial park.

Appliance of medical genogram and social network theory in cardiovascular health promotion intervention and non-laboratory-based risk monitoring

Objective: This study aims to determine the number of patients, as well as their nuclear and extended family members with or without established cardiovascular disease, at the baseline age of ≤20 along with the family members under 20 years old, who could benefit from a potential sociocultural, environmental and household related cardiovascular health promotion interventions in a social tie network, which enriches the explanation of human behavior by explicitly taking its social structure into account. Methods: This study is a part of a clinical trial-educational family-centered health intervention on patients undergoing percutaneous coronary intervention and their household members. Ethical clearance was obtained from Iran National Committee for Ethics in Biomedical Research, and approved by the Iranian Registry of Clinical Trials. The study was conducted from October 2021 to March 2022. It involved a total of 33 patients. The demographic information was gathered from a self-administered questionnaire and the information was analysed via medical genogram and social network analysis model at individual and group level. Results: Overall, via only 33 patients and their significant other (n=66), we got access to the medical history of 429 nuclear and extended family members.

Modelling cabotage. Coastal navigation in the western Mediterranean Sea during the Early Iron Age

The study of population mobility and its impact on societies throughout history has undergone significant changes as a field in recent years. When we refer to maritime mobility applied to Early Iron Age (EIA) societies in the Mediterranean context, the predominance of coastal or cabotage navigation activity allows us to define this mobility as maritime transhumance. Over time, this mobility has defined the degree of connectivity of some areas with others, thus marking the cultural interactions of each space. This paper introduces the development of a working methodology through the creation of a GIS navigation model complemented by a social network analysis model (SNA). The nautical capabilities of the Gyptis—a replica of the sixth-century BC Archaic Greek Jules-Verne 9 shipwreck (from Marseille, France)—are applied in the context of the western Mediterranean Sea, along with forecasting parameters such as wave height, wind force and direction, combined with coastal visibility. The results reveal a workflow applicable to the analysis defining the physical and social characteristics applied to a specific vessel. While GIS enables mathematical calculations of navigational difficulty, SNA helps explore the relationship between communities connected by coastal navigation. In conclusion, the combination of GIS and SNA allows a closer examination of micro-regions. The concept of micro-regions integrates the Mediterranean Sea as a whole, as well as interactions between the maritime cultures that define it.

Impacts of OFDI on Host Country Energy Consumption and Home Country Energy Efficiency Based on a Belt and Road Perspective

Under the Belt and Road concepts of mutual benefit and win–win cooperation, China is strengthening its energy cooperation with other countries. We used several econometric models and social network analysis models to study the impacts of China’s outward foreign direct investment (OFDI) on the host and home countries. We first examined China’s OFDI location preference and analysed the effects of OFDI on energy consumption in host countries. Meanwhile, we observed the impact of the reverse spillover effect of OFDI on China’s energy efficiency. The results indicate that (1) the impact of China’s OFDI on energy consumption in host countries has been lower than that on neighbouring countries, and increased significantly after 2014. (2) The space network of energy consumption in Belt and Road countries has a strict hierarchical structure. However, it was disbanded by the Belt and Road policy in 2014. The network centres are situated primarily in Middle Eastern and European countries, and the network’s periphery is mainly in South-East and West Asian countries. (3) The reverse spillover effects of OFDI, FDI, domestic R&D absorptive capacity, human capital, and financial development levels are conducive to improving China’s energy efficiency whereas regional professionalism does the opposite.

Evaluation and optimization analysis of high-speed rail network structure in Northeast China under the background of northeast revitalization

The construction of high-speed rail (HSR) network has promoted the social-economic ties of cities, accelerated the compression of time and space, and changed the pattern of regional development. In this paper, with the adoption of the operation frequency data of HSR from 12306 website, and based on the HSR connection strength model and social network analysis model, as well as according to the HSR connection strength, HSR network density, centrality, agglomeration subgroup, and other indicators, we analyzed the characteristics of HSR network structure in Northeast China. Results show that the number of HSR cities in Northeast China is small, cities in HSR network generally exhibit weak connectivity, and the existence of HSR network marginalizes cities such as Ulanhot, Baicheng, and Songyuan, which significantly reduce the overall network connectivity of Northeast China. The overall centrality of HSR network in Northeast China is characterized by “one axis, four edges”; specifically, the one axis is located in Harbin-Dalian transportation line and the four edges are located on both sides of the main axis of Harbin-Dalian transportation line. Eight agglomeration subgroups (four double city subgroups and four multi city subgroups) have formed in Northeast China. The core status of Shenyang in HSR network is improved significantly, and “one axis and two wings” HSR network in Liaoning Province is improved significantly. With the gradual expansion of Chaoyang-Fuxin, Dandong-Benxi, and Jilin-Yanji branch networks, the “point axis” HSR network mode in Northeast China has gradually developed and matured. In the future, it is recommended to rely on eight agglomerating subgroups to encrypt HSR network structure, create secondary node central cities, and gradually build a new pattern of opening up in Northeast China.

A guide to choosing and implementing reference models for social network analysis.

ABSTRACTAnalysing social networks is challenging. Key features of relational data require the use of non‐standard statistical methods such as developing system‐specific null, or reference, models that randomize one or more components of the observed data. Here we review a variety of randomization procedures that generate reference models for social network analysis. Reference models provide an expectation for hypothesis testing when analysing network data. We outline the key stages in producing an effective reference model and detail four approaches for generating reference distributions: permutation, resampling, sampling from a distribution, and generative models. We highlight when each type of approach would be appropriate and note potential pitfalls for researchers to avoid. Throughout, we illustrate our points with examples from a simulated social system. Our aim is to provide social network researchers with a deeper understanding of analytical approaches to enhance their confidence when tailoring reference models to specific research questions.

A psychodiagnostic tool for the structural assessment of the social network in patients with schizophrenia: validity and psychometric properties

The aim of the study was to develop a new valid psychometric diagnostic tool for a multi-factor social network assessment of schizophrenic patients, called «The structural assessment of the social network of schizophrenic patients». The new development is based on the social network analysis model elaborated by S. L. Phillips (1981) and translated into Russian by Gurovich I. Ya. et al. (2007). The authors of this article additionally developed an algorithm for assessing the activity of patients on social networks of the Internet. Reducing non-informative variables and conducting a confirmatory factor analysis in a sample of 265 observations of schizophrenic patients (F20.0) aged from 18 to 55, recruited in four medical organizations (145 patients admitted to the hospital and 55 outpatients), resulted in determining a four-factor structure of the patients’ social network: «Objective parameters», «Internet activity», «Emotional aspect of social support» and«Reciprocal support». The method has demonstrated high internal and external validity, as well as applicability in the clinical practice in schizophrenia due to the low resource consumption and compact applicability. Measurable factor indicators of the patients’ social networks obtained by using the structural assessment of the social network of schizophrenic patients allow to determine the targets for psychocorrectional interventions and to increase the effectiveness of psychosocial rehabilitation. In addition an automated method for calculating final indicators has been developed, as well as manual, practical recommendations and corresponding printed forms.