Social Network Analysis Approach Research Articles

How melon farmers’ networks enable greenhouse technology adoption? A social network analysis

ABSTRACT Purpose The study aims to investigate the connections and interactions of the melon farmers in the social network related with greenhouse adoption for the cultivation. Design/Methodology/Approach This research employed the Social Network Analysis (SNA) approach from the field survey data in the dry-land areas in Indonesia. Findings The study results indicate the presence of polarity within the social networks of farmers. The proliferation of innovations is motivated by individuals who actively develop their skills and knowledge, employing a combination of experimentation and online resources to enhance their capabilities. Dissemination is a result of the extensive collaboration among farmers who are located near each other. In addition, the trust and connection among farmers who have successfully experimented and achieved results fosters a collaborative and knowledge-sharing environment. Practical Implications The results of this study provide implications for appropriate rural extension and community development strategies. Theoretical Implications This research broadens the scope of innovation diffusion theory by examining social networks and their role in the proliferation of modern agricultural cultivation technology in marginalized areas. Originality/Value The study presents empirical evidence that highlights the successful adoption of modern agricultural cultivation technologies by fringe groups. It emphasizes the significance of locally established, self-sustaining social networks in facilitating this proliferation, marking the novelty of this study, especially in developing countries.

Centrality in Collaboration: Community Detection for Oncology Researchers

Background: Cancer patients who do not respond to standard treatments often become candidates for clinical trials. It is known that the collaboration networks between oncologists is a primary factor for further engagement in subsequent trial enrollment. Social network analysis (SNA) and community detection algorithms can be used to explore collaboration patterns using data from the Princess Margaret Clinical Research Record and Cancer Registry. Objectives: The data consists of 2970 patients in 515 clinical trials between January 2016 to December 2018. We are interested in the referral patterns among patients enrolled in multiple trials. Among 389 patients enrolled in more than one of 288 trials, we aim to identify collaboration networks based on intervention type. Methods: We applied community detection algorithms, including Girvan-Newman and Louvain, and compared them to an author-developed algorithm that utilizes degree centrality and directionality in participant enrollments to identify influential interventions. Community detection was assessed by superimposing sociograms and visualizing the contrast between the in-degree/out-degree distribution of participant enrollments. Results: Application of Girvan Newman, which incorporates edge betweenness but not degree centrality, and Louvain, which does not consider directionality, did not yield informative structure for oncology collaboration networks. The algorithm developed by the authors found intuitively descriptive communities that were supported by the sociograms and visualized in-degree/out-degree distribution. Conclusions: SNA approaches utilizing a combination of degree centrality and edge betweenness can interpret oncology collaboration networks. With this perspective, it is possible to delve deeper into patient engagement and improve the organizational design of clinical trial referrals.

Leveraging the social networks of informal healthcare providers for universal health coverage: insights from the Indian Sundarbans.

India's healthcare landscape is characterized by a multitude of public and private healthcare providers, yet its health systems remain weak in many areas. Informal healthcare providers (IHPs) bridge this gap, particularly in rural India, and are deeply embedded within local communities. While their importance is widely recognized, there is a knowledge gap regarding the specifics of their social networks with actors in health systems. The aim of this study was to map the social networks of IHPs to elucidate the type and nature of their relationships, in order to explore opportunities for intersectoral collaboration to achieve universal health coverage (UHC). We have adopted the social network analysis (SNA) approach using qualitative ego-network methodology to evaluate the types and strengths of ties in the Indian Sundarbans. A total of 34 IHPs participated in the study. Qualitative data were analysed using NVivo10 and Kumu.io was used to visualize the social networks. Results show that the 34 IHPs had a total of 1362 ties with diverse actors, spanning the government, private sector and community. The majority of the ties were strong, with various motivating factors underpinning the relationships. Most of these ties were active and have continued for over a decade. The robust presence of IHPs in the Indian Sundarbans is attributable to the numerous, strong and often mutually beneficial ties. The findings suggest a need to reconsider the engagement of IHPs within formal health systems. Rather than isolation, a nuanced approach is required based on intersectoral collaboration capitalizing on these social ties with other actors to achieve UHC in impoverished and underserved regions globally.

Fuzzy centrality measures in social network analysis: Theory and application in a university department collaboration network

The motivation behind this research stems from the inherent complexity and vagueness in human social interactions, which traditional Social Network Analysis (SNA) approaches often fail to capture adequately. Conventional SNA methods typically represent relationships as binary or weighted ties, thereby losing the subtle nuances and inherent uncertainty in real-world social connections. The need to preserve the vagueness of social relations and provide a more accurate representation of these relationships motivates the introduction of a fuzzy-based approach to SNA. This paper proposes a novel framework for Fuzzy Social Network Analysis (FSNA), which extends traditional SNA to accommodate the vagueness of relationships. The proposed method redefines the ties between nodes as fuzzy numbers rather than crisp values and introduces a comprehensive set of fuzzy centrality indices, including fuzzy degree centrality, fuzzy betweenness centrality, and fuzzy closeness centrality, among others. These indices are designed to measure the importance and influence of nodes within a network while preserving the uncertainty in the relationships between them. The applicability of the proposed framework is demonstrated through a case study involving a university department's collaboration network, where relationships between faculty members are analyzed using data collected via a fascinating mouse-tracking technique.

Integrated Systematic Framework for Forecasting China’s Consumer Confidence: A Machine Learning Approach

This study aims to introduce a novel approach for predicting China’s consumer confidence index (CCI), a key economic indicator that reflects consumers’ confidence in current and future economic conditions. While traditional statistical models and economic indicators are the primary tools for forecasting CCI, their reliance on linear assumptions limits their ability to capture the complex, dynamic relationships inherent in economic systems. In response, this study proposes a two-step method that integrates social network analysis (SNA) and machine learning (ML) to enhance prediction accuracy by accounting for the nonlinear interactions and systemic interdependencies that drive consumer confidence. The use of SNA enables the identification of critical variables and their interconnected roles in shaping consumer sentiment, while ML models, specifically the gradient boosting decision tree (GBDT), leverage these relationships to provide more precise predictions. Utilizing monthly data from 1999 to 2023, the combined SNA and GBDT approach significantly improves the accuracy of CCI forecasts, particularly during periods of high volatility. The results of this study hold substantial value for policymakers, market analysts, and economists, as they offer a systems-oriented framework for economic forecasting. By demonstrating the effectiveness of combining SNA with ML technologies, this research not only advances the methodological toolkit for economic forecasting, but also provides a new lens through which the complex, adaptive nature of economic systems can be better understood and managed. This integrated approach paves the way for future developments in forecasting models that more accurately reflect the evolving dynamics of consumer confidence in a rapidly changing economic environment.

The spatial correlation of economic institutional change in China and its impact on economic growth: A social network analysis approach.

Economic institutional change is a vital driving force behind the rapid rise of China's economy. However, the incremental approach to economic institutional change has caused unbalanced transformation and economic growth. To this end, we adopted the entropy method to measure the economic institutional change index, and employed social network analysis to reveal its spatial correlation characteristics. We then applied QAP analysis to empirically demonstrate the impact of China's economic institutional change on regional disparities in economic growth. The findings indicated a gradual increase in the level of economic institutions over time and a spatial gradient between the eastern, central, and western regions. Moreover, the spatial correlation network of China's economic institutional change is stable and gradually improving. Nevertheless, the role of provinces in the process of economic institutional change varies: the eastern coastal provinces play a dominant role, the central and western provinces benefit to a lesser extent, and some provinces in northeastern China play a "bridging" and "intermediary" role. Regional differences in China's economic institutional change have widened the regional disparities in China's economic growth, and the impact of each dimension of economic institutions on regional disparities in economic growth is characterized by phases.

Social network analysis approach to identify agricultural key communicators

Aim: This paper employed Social Network Analysis approach to visualize and calculate different social network metrics. It identifies key communicators who play pivotal roles in information flow. Methodology: Research was conducted at Jammulapalem and Perali villages of Bapatla district, Andhra Pradesh, India, during 2022-23 using an exploratory research design. A total of 120 farmers Eigen vector were selected using simple random technique, and data was collected using a well-developed interview schedule. Network metrics such as Degree centrality, betweenness centrality, and eigen vector centrality were computed to evaluate the network structure using R software (version 4.3.1). R packages, namely igraph, statnet and network D3, were used for network creation, analysis and visualization to identify the influential nodes. Results: The study revealed a complex web of relationships among various stakeholders within the agricultural network through a network graph, identifying key communicators with the highest Degree centrality. Interpretation: The focal points identified through Social Network Analysis represent a specific demographic and socio-economic group, typically aged between 35 and 55, primarily medium-scale farmers,with landholdings spanning 10 to 25 acres and high annual income, with educational backgrounds ranging from high school to pre-university college, and wield significant influence within their local communities. They require sensitization, training, practical demonstrations, and personalized support to effectively disseminate agricultural information. Key words: Agricultural Information System Network, Centrality measures, Information Sources, Key Communicators, Network visualization, Social Network Analysis

Collaboration and support between Chilean schools from an egonet mixed-method approach

This study aims to characterise inter-organisational collaboration and support networks between schools in a privatised, marketised, and competitive school system. It is located within and contributes to a body of literature on school networks, which has primarily focused on studying the architecture and conditions for the sustainability of the arrangements where collaboration and support between schools are meant to take place. However, there has been less focus on examining the partnerships themselves, particularly those formed organically by schools. This is an exploratory study that takes a novel convergent mixed-method egonet Social Network Analysis approach. Primary data were produced from interviews with sixteen primary school headteachers from a local area in Santiago, Chile. Participants were encouraged to name and reflect on other schools with which their schools have an inter-organisational relationship. Insights show that schools draw on both mandated and organically formed inter-organisational relationships to collaborate and support each other. These ties tend to be built with schools alike and geographically close. Some of the main drivers to turn to others are ensuring the sustainability of schools, generating coordinated responses to policy mandates, sharing practice and knowledge, and ensuring students’ schooling trajectories. This study, situated within the broader context of school networking literature, proposes that schools exist within multiple networks, with various connections serving different purposes, some of which are rarely acknowledged. It suggests that while formal school networks and governance structure are important, they are insufficient to represent the inter-organisational relationships in which schools are involved.

Workplace Hazard Identification through Near-Miss Reports: A Social Network Analysis

Occupational Safety and Health (OHS) is an effort to create a safe and healthy working environment. The company, which is engaged in power generation, experienced many near-miss cases from 2016 to 2023. Near-miss incidents are an early signal of the potential for severe accidents in the future if not resolved immediately. Identifying and addressing potential hazards early is necessary to prevent fatal work accidents. This study aims to analyze the reporting pattern of near-miss incidents by company employees through the IZAT application (Zero et al.) with a social network analysis (SNA) approach. This method identifies relationships and interaction patterns between employees in reporting near-miss incidents, thus revealing objects that need special attention. Data was obtained from September 2020 to July 2023. The analysis showed that 276 employees actively reported various near-miss incidents through IZAT (Zero Accident Assistant Application). This pattern reflects employees' sensitivity to potential hazards in their work environment. The findings of this study make a positive contribution to the improvement of the occupational safety and health management system in the company regarding the mitigation of potential hazards based on incoming report data. The recommendations provided are related to occupational safety aspects that need to be prioritized for management improvement. The implication of this research is to increase employee awareness in identifying and reporting potential hazards so as to prevent work accidents in the future.

Evaluation of Public Transportation System through Social Network Analysis Approach

In response to the phenomenon of global warming, the transportation sector aims to mitigate carbon emissions by promoting the use of public transportation. This study employs social network analysis to propose effective improvements to the public transportation system, focusing on bus stop locations and route networks in Hwaseong City, South Korea. Two networks were constructed based on existing public transportation routes and usage data at each bus stop. The findings and implications are as follows: Analyzing the public transportation network from a network perspective can effectively contribute to improving the public transportation network route system. By evaluating centrality and brokerage for the existing routes, it is possible to identify inefficient routes and develop efficient route modification plans. Based on actual usage patterns, excessive bus supply and unnecessary bus stop locations can be identified, allowing for the establishment of appropriate operational plans. This can lead to improved operational efficiency and cost savings. Rational route design and operational planning can enhance public transportation services and promote increased use of public transportation. Ultimately, this contributes to sustainable development through carbon reduction in the transportation sector.

Analysis of lithium trade patterns and influencing factors in the regions along the "Belt and Road".

Lithium has broad applications in several emerging industries and fields, including high energy batteries, energy storage, aerospace, and controlled nuclear reactions. Currently, the discrepancy between the supply and demand for lithium resources increases, and its distribution is uneven. Within the framework of the "Belt and Road" Initiative, the lithium trade pattern evolves constantly. However, the trade pattern of lithium in the nations along the "Belt and Road" is likely to face substantial repercussions in modern world of unilateral protectionism and geopolitical conflicts. Taking the social network analysis approach as a tool, this study first examines the characteristics of the lithium trade network structure as it has evolved over the years in the Belt and Road countries, from 2000 to 2022. Additionally, this study uses the quadratic assignment problem approach to analyze the factors influencing the evolution of the lithium trade network. The study shows that: (1) The spatial patterns of import and export trade network of lithium in countries along the route has a certain path dependence. And the market is mainly concentrated in East Asia, Central and Eastern Europe, South America and Southeast Asia. (2) The network density of the countries along the route has increased year after year, but it remains low. And the fluctuation of the network's reciprocity has increased, with a huge magnitude of variation. The number of core countries in the network has decreased over time, but the core-periphery structure has stayed largely steady. China, Chile, and South Korea are the network's main node countries. (3) Regarding the influencing factors, the differences in economic and technological development between these countries have a beneficial impact on the formation of lithium trade; whether or not regional trade agreements have been signed, the differences about average tax rates for mineral products, bordering countries, and similar languages and cultures are all conducive to the establishment of close trade links. The contribution of this essay is of paramount importance for understanding different countries' role along the Belt and Road in the lithium trade network pattern, and promoting regional trade cooperation.

Empowering Equality Law With Nlp: A Social Network Analysis Approach to Drive Legal SDGS

Objectives: The purpose of this study is to use social media analysis to understand people's opinions about legal equality. Theoretical Framework: The study builds on social studies theory and existing literature to explore the complexity of society's reaction to legal equality. It focuses on how public opinion evolves over time and the factors influencing these changes, including language, age, and gender. Method: Data was gathered from various social media platforms on September 8, 14, and 15, 2023, using the Talkwalker Social Intelligence Platform. The analysis involved monitoring public opinion trends and performing a theme analysis to identify recurring themes in the discourse. Results and Discussion: The findings revealed essential trends in societal reactions to legal equality. Notable changes in perceptions of specific issues were observed, and prominent aspects of legal equality, such as language, age, and gender, were identified. These results provide a comprehensive understanding of how society perceives legal equality and the dynamics influencing these perceptions. Research Implications: The study enhances existing literature and advances social studies theory by offering insights into the complexity of societal reactions to legal equality. It also demonstrates the potential of social media analysis using Social Network Analysis (SNA) and Talkwalker-type tools to understand how social interactions shape opinions and to inform policy planning. Originality/Value: This research illuminates the social dynamics of legal equality and showcases the effectiveness of combining SNA with social media analysis tools. The findings offer valuable contributions to theoretical and practical discussions on legal equality and policy-making, particularly in the context of promoting the SDGs.

Dynamic clustering of Asian regionalisms: Empirical testing of United Nations multilateral treaty participation by social network analysis

This paper investigates the evolving clustering and historical progression of “Asian regionalisms” concerning their involvement in multilateral treaties deposited in the United Nations system. We employ criteria such as geographic proximity, historical connections, cultural affinities, and economic interdependencies to identify twenty-eight candidate countries from East Asia, Southeast Asia, South Asia, and Central Asia for this empirical testing. Using a social network analysis approach, we model the network of these twenty-eight Asian state actors alongside 600 major treaties from the United Nations system, identifying clusters among Asian states by assessing similarities in their treaty participation behavior. Specifically, we observe dynamic changes in these clusters across three key historical eras: Post-war reconstruction and transformation (1945–1968), Cold War tensions and global transformations (1969–1989), and post-Cold War era and globalization (1990–present). Employing the Louvain cluster detection algorithm, the results reveal the evolution in cluster numbers and changes in membership status throughout the world timeline. The results also identify the current situation of six distinct Asian clusters based on states’ inclinations to engage or abstain from multilateral treaties across six policy domains. These findings provide a foundation for further research on the trajectories of Asian regionalisms amidst evolving global dynamics and offer insights into potential alliances, cooperation, or conflicts within the region.

Poet-Composer Collaborations in Modern Egyptian Song: A Social Network Analysis Approach to Music History

In this paper I formulate and apply a social network analysis methodology for understanding the history of song on Egyptian radio. Music is a massively relational cultural form, involving interactions among composers, poets, arrangers, conductors, and performers, among others. The reality of music history thus emerges as a complex network of relationships, unfolding and changing over time. Song production, in particular, centers on poet-composer-singer collaborations. Many Arab music histories highlight narratives of the stars, presented in historical and cultural context, but neglect the broader network of productive relationships. However many important musical figures are not celebrities, and the full complexity of the non-linear network can only be grasped holistically, including big data empirical analysis, not pointillistically through case studies of celebrities. Such holistic analysis can reveal surprising emergent, structural patterns that are not apparent in any single narrative. Social network analysis (SNA) offers a powerful suite of tools enabling such an approach, including metrics for centrality and the detection of cohesive subgroups. Starting with a large dataset of songs broadcast on Egyptian radio, I extract a network of poet-composer collaborations, and apply SNA algorithms to reveal its social structure. I then interpret that structure in light of wider socio-cultural and historical factors. In this way, my paper both sheds light on Egypt’s musical history, and supplies a model and method that may be applied, mutatis mutandis, to other musical domains.

Social Network Analysis: Understanding User Behavior in Threads

The presence of threads has enhanced users' ability to share stories, information and views in greater detail and effectively in the online world. Social media platforms allow users to interact, share content, and connect online. Therefore, the purpose of this research is to create a Social Network Analysis (SNA) network visualization and categorize the data to contribute to the understanding of user interaction on this platform. The research method in this study is the Social Network Analysis (SNA) approach to analyze the relationship between individuals in social networks. The results show that user participation in the Threads platform has a significant positive impact. The data shows a high level of participation, providing an understanding of user engagement in various topics. Social network analysis revealed characteristics of user interactions, such as the number of nodes, average connectedness, and relationship complexity. In addition, word categories and network visualizations provide insights into relationship patterns and topics in conversations.

Exploring emergent leadership in agile teams: network dynamics, roles and skills at the individual level

PurposeLeadership research has traditionally focused on formal leadership; however, leaders may emerge in informal settings in self-managed teams, and little is known about who emergent leaders are and what their characteristics are. This study investigates emergent leaders' behaviors, roles, skills, and leadership style, drawing on a multi-method approach.Design/methodology/approachWe first identify emergent leaders using social network analysis and aggregation approaches. Second, we investigate emergent leaders' characteristics using interviews with forty agile team members in five organizations.FindingsResults indicate different roles of emergent leaders (i.e. coach, liaisons), leadership styles (i.e. supportive), skills (i.e. culturally intelligent, strategist), and influencing factors (i.e. personality, technical knowledge, social circle).Originality/valueWe contribute by identifying emergent leaders through multiple identification methods (i.e. network analysis, aggregation), and then through identifying their various characteristics, we contribute to leadership literature as well as idiosyncrasy-credit theory. We also add to agile-leadership theory, showing that multiple informal leaders may emerge within agile teams. Finally, our findings have practical implications for self-managed teams, informal group settings, organizational change professionals, and organizations with horizontal structures.

Social network dynamics in rural public spaces of multi-ethnic settlements: A case study from Tongren, China

In multi-ethnic rural settlements, a diversity of social relationships gives rise to various public spaces. The integration of cultures, propelled by urbanization, poses challenges to the unique characteristics of these rural public spaces. This process leads to issues such as the loss of cultural diversity, increased homogeneity of spaces, uneven distribution of public services, and low efficiency in space utilization, which seriously interfere with the healthy and sustainable development of rural social systems in multi-ethnic rural areas. This study, focusing on the rural public spaces in Tongren's multi-ethnic settlements, employs a social network analysis approach to investigate the implications of these spaces for the stability of rural community life and the preservation of cultural identities. A model for analyzing social networks in rural public spaces was developed, emphasizing overlapping patterns of user groups, activities, and spatial entities. Analysis of the social networks was conducted in three domains: emotional support, material exchange, and information dissemination. A semantic model was also constructed to understand the usage and structural networks within these public spaces. Comparative analyses between the usage and structural networks of rural public spaces were conducted to develop a comprehensive structural analysis system for these areas. Application of this system in analyzing the rural public spaces of Tongren revealed distinct findings. The connectivity of the structural network in Dongganmu Village's public spaces was found to be lower than that of the usage network, indicating that social factors heavily influence villagers' interactions. Furthermore, the number of small groups within the usage network was fewer than in the structural network, reflecting a lack of hierarchy in public space organization. Villagers’ preferences for public spaces were primarily influenced by cultural beliefs rather than spatial accessibility. The insights garnered from this study not only contribute to the understanding of social network structures in rural public spaces within multi-ethnic settlements but also provide a foundation for informed decision-making regarding the sustainable development of these areas.

Implementasi SNA dalam Menilai Popularitas Platform Pemesanan Makanan Online Berbasis Percakapan Twitter

This research aims to understand the popularity of online food ordering platforms in Indonesia, although the data taken in Indonesian includes allied languages. This research combines the SWARA, WASPAS, and Social Network Analysis (SNA) approaches to achieve its objectives. The problem formulation proposed is how to measure and analyze the popularity of online food ordering platforms based on interaction data on Twitter social media. The SWARA method is used to determine the weight of the evaluation criteria, while WASPAS is applied to rank online food ordering platforms. Meanwhile, SNA is applied to analyze interaction patterns and user preferences for various online food ordering platforms. Twitter was chosen as the main data source because it provides direct conversation data and real depictions, allowing for in-depth and accurate analysis. Through Twitter, research can capture the dynamics of user interactions and how they communicate about various online food ordering platforms. This research aims to provide a strong foundation for decision makers in designing more effective marketing strategies and provide insight into the dynamics of competition in the online food ordering platform industry. The research results show that SNA-based analysis is able to clearly identify interaction patterns and user preferences.

A social network analysis approach to assess COVID19-related disruption to substance use treatment and informal social interactions among people who use drugs in Scotland.

To assess the extent of Coronavirus-related disruption to health and social care treatment and social interactions among people with lived or living experience of substance use in Scotland, and explore potential reasons for variations in disruption. Cross sectional mixed methods interview, incorporating a social network 'egonet interview' approach asking about whether participants had interactions with a range of substance use, health, social care or third sector organisations, or informal social interactions. Five Alcohol and Drug Partnership Areas in Scotland. 57 (42% women) participants were involved in the study, on average 42 years old. Five-point Likert scale reporting whether interactions with a range of services and people had gotten much better, better, no different (or no change), worse, or much worse since COVID19 and lockdown. Ratings were nested within participants (Individuals provided multiple ratings) and some ratings were also nested within treatment service (services received multiple ratings). The nested structure was accounted for using cross classified ordinal logistic multilevel models. While the overall average suggested only a slight negative change in interactions (mean rating 2.93), there were substantial variations according to type of interaction, and between individuals. Reported change was more often negative for mental health services (Adjusted OR = 0.93 95% CI 0.17,0.90), and positive for pharmacies (3.03 95% CI 1.36, 5.93). The models found between-participant variation of around 10%, and negligible between-service variation of around 1% in ratings. Ratings didn't vary by individual age or gender but there was variation between areas. Substance use treatment service adaptations due to COVID19 lockdown led to both positive and negative service user experiences. Social network methods provide an effective way to describe complex system-wide interaction patterns, and to measure variations at the individual, service, and area level.

Networks for Integrated Wildfire Management: A Social Network Analysis Approach

Wildfires, exacerbated by climate extremes, pose escalating global threats. Despite advancements in technology, current wildfire management policies lack specific details about integrated management and stakeholder engagement. This study provides empirical evidence on how five European wildfire management locations align with networked governance models by analyzing how their stakeholder networks are configured through social network analysis (SNA). This is measured by assessing network configuration and stakeholder group profiles. Measurements at the network level assess the size, density, diversity of sectors, scalar levels of operation, and phases. At stakeholder level, relevance is assessed by measuring in degree, eccentricity, closeness centrality, and betweenness centrality. Analysis qualitatively assesses the network profile, leading to the discussion on the primary factors influencing network configurations and the potential outcomes of these attributes