Application Of Social Network Analysis Research Articles

Advancing the Measurement of Social Functioning in Schizophrenia: Applications of Egocentric Social Network Analysis.

People with schizophrenia face challenges with forming and maintaining social relationships, often resulting in poor social functioning. Commonly used measures of social functioning provide broad information relating to social relationships, but they do not adequately capture information regarding network structure and characteristics of network members. One method that can assess these more detailed aspects of social networks and provide a more comprehensive understanding of social functioning deficits is egocentric social network analysis (SNA). SNA is a scientific discipline that uses principles of network science and graph theory to analyze social relations quantitatively. Even though some types of SNA have been applied in prior schizophrenia studies, its application as a framework to measure social functioning has been extremely limited. Therefore, this article aims to formally introduce SNA and select quantitative SNA metrics, including measures of network composition, structure, homophily, and centrality, to schizophrenia researchers as novel ways of measuring components of social functioning. To demonstrate the application of SNA, we provide illustrative examples of the SNA metrics and graphical diagrams of social networks for two individuals with schizophrenia.

A network of designs: studying Early Lapita exchange networks in the Bismarck Archipelago, Papua New Guinea through Social Network Analysis

ABSTRACTThe application of Social Network Analysis to the study of archaeological networks has become increasingly common around the world, with a proven track record of processing large, complex, spatial and temporal archaeological datasets. This study builds upon previous network‐based analyses of interaction between communities of the Lapita Cultural Complex, with a specific focus on the Early Period (c.3300/3200–3100 calBP) in the Bismarck Archipelago, Papua New Guinea. Employing motif inventories from 13 Early Lapita Period assemblages, motif similarity and Centrality Analysis are undertaken, with the results compared to data from portable material culture, particularly obsidian and pottery, to further define the networks of interaction that linked communities during this period. We conclude that motif similarities and Centrality Analysis scores show good agreement with patterns of interactions established from the analysis of other types of portable material culture, which together support the existence of northern west to east and southern obsidian distribution networks, comprised of communities that employed unique types of interaction tailored towards their own cultural and societal circumstances and needs. Finally, we further conclude that these two networks may have arisen during the initial formation of the Lapita Cultural Complex, as populations established new social connections with other settler communities and incumbent populations across the region to survive in a new and foreign environment.

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.

Social Network Analysis: A Survey on Process, Tools, and Application

Due to the explosive rise of online social networks, social network analysis (SNA) has emerged as a significant academic field in recent years. Understanding and examining social relationships in networks through network analysis opens up numerous research avenues in sociology, literature, media, biology, computer science, sports, and more. Therefore, certain studies review and discuss some research verticals of SNA, such as viral marketing, information diffusion, clustering, link prediction, and so on, to provide background knowledge and understanding. These studies still lack the SNA process, tools, and practical aspects in multidisciplinary applications. Inspired by these facts, we have discussed the background, process, tools, and application of SNA. First, we have presented a detailed description of the SNA process. Thereafter, we presented a comparative analysis of SNA tools and languages. Finally, we have discussed the various applications corresponding to SNA research verticals.

Application of Social Network Analysis for Comparison and Ranking of Internet Service Providers

In this digital era, the Internet has become a basic necessity in life. This has had a significant impact on the growth of internet service provider (ISP) companies in Indonesia. Comparison and ranking of ISPs is needed to make it easier for users to choose services according to their needs as well as to encourage healthy competition between ISPs in improving their services. The problem is ranking ISPs using conventional methods (surveys) to obtain primary data is expensive and takes a long time. On the other hand, Social Network Analysis (SNA) is a method that has been widely used to understand customer desires by extracting information from social media. This information is in the form of User Generated Content (UGC), namely track records left by customers on social media. This research aims to measure the ISP rankings of Indihome, Biznet and FirstMedia using UGC data. The research method used is to collect consumer tweet data rapidly, carry out preprocessing to eliminate irrelevant data and apply SNA, including network structure analysis in the form of visualization and network property analysis with the Gephi application, as well as network content analysis in the form of sentiment analysis and WordCloud analysis. The number of dominant network properties and sentiment analysis calculates ISP ranking. Apart from that, the results of this SNA are in the form of recommendations for ISPs to improve services to customers.

The effects of the COVID-19 pandemic on connectivity, operational efficiency, and resilience of major container ports in Southeast Asia

The COVID-19 pandemic in 2020 has had significant impacts on the connectivity and performance of container ports in Southeast Asia. The application of social network analysis (SNA) in conjunction with key indicators, such as container throughput, total capacity of ships, number of ship calls, and berth duration, provides a comprehensive understanding of the development of the regional port system. While major ports in the region, namely Singapore, Port Kelang, Manila, and Laem Chabang, have exhibited signs of recovery owing to the expeditious and timely implementation of targeted policies to mitigate the adverse effects of the pandemic, smaller ports in the region, such as Bangkok, Penang, Tanjung Priok, and Tanjung Perak, are grappling with enduring repercussions and encountering challenges in resilience. These challenges stem from inadequate infrastructure, limited accommodation capacity, low loading/unloading productivity, and other significant risk events associated with the pandemic, all of which have weakened their competitiveness. In contrast, ports in Vietnam, including Cai Mep, Hai Phong, and Ho Chi Minh, as well as Malaysia's Tanjung Pelepas, have demonstrated that they are less impacted by the COVID-19 pandemic. This can be attributed to the sustained growth of their respective economies and the continued surge in domestic production to meet the unabated demand for exports. Based on these findings, several policy recommendations can be suggested for port authorities to effectively manage and mitigate the impact of risky events, such as pandemics, in the future while also enhancing the competitiveness of ports.

The Evolution of Social Network Analysis in Public Administration

Despite the importance of social network analysis (SNA) in public administration, comprehensive reviews on this topic are rare. This study aims to fill this gap by focusing on the application of SNA in network research within public administration. This research analyzes 144 empirical papers from top public administration journals (January 1997–January 2022), utilizing bibliometric and content analyses. The study identifies social capital, the Advocacy Coalition Framework (ACF), the ecology of games framework, and resource dependence theory as the predominant theoretical foundations of SNA. The primary focus of these works is on collective action problems and public service delivery, with a trend towards the integration and comparison of theories. Through content analysis, we find a notable shift in SNA application from descriptive analysis to network inference, predominantly using Exponential Random Graph Models (ERGMs). It also reveals the growing significance of network visualization in SNA. The study concludes with potential future directions for SNA in public administration, highlighting its evolving role and significance.

Mapping routine interactions in port operations: Insights from social network analysis

Social network analysis proves to be a valuable instrument for comprehending and visually representing intricate relationships among diverse entities within a network. This study aims to explore the application of social network analysis within the context of hinterland port operations. In pursuit of this aim, we examined the interactions amongst various stakeholders, including port authorities, truck drivers, port agent companies, vigilant guards, import/export firms, and terminal operators. As a result, we unveiled patterns, pinpointed bottlenecks, and unearthed opportunities to amplify efficiency and collaboration within this system. This inquiry contributes to the existing body of knowledge concerning the utilization of social network analysis within the maritime industry while also providing insights to enhance port performance.

‘We knew a cyclone was imminent’: Hazard preparedness and disaster management efficiency nexus in coastal Bangladesh

Natural hazard-prone countries in the global south adopted an anticipatory-preparedness approach in disaster risk reduction (DRR) by shifting away from the response and rehabilitation approach. It was highly associated with the policy recommendations of the United Nations Office for Disaster Risk Reduction (UNDRR) framework. Nevertheless, challenges remained in managing disaster risks due to heterogeneous efficiency among disaster managers. This research examines the institutional effectiveness of the disaster management agency in cyclone-prone areas in southwestern coastal Bangladesh through network analysis. This research also explores institutional efficiency's impact on household preparedness in mitigating losses. We collected two distinct data sets from the local Union Disaster Management Committee (UDMC) and households in the same operational area of UDMC that were at risk of cyclonic disasters. To evaluate the effectiveness of local disaster management institutions, we conducted structured interviews with 336 representatives, spanning eight UDMCs in the cyclone-prone regions. To assess the impact of institutional performance (specifically, the UDMCs) on household disaster preparedness and loss mitigation, we collected data from 696 at-risk households. These households were located within the operational zones of the eight UDMCs, and the data was obtained using a structured questionnaire. Based on the application of social network analysis, our findings demonstrated that UDMC members were more active in the aftermath of disasters, as confirmed by three centrality measures - degree, eigenvector, and betweenness. Consequently, upon employing the Negative Binomial and Poisson regression models, we found a positive association between household engagement with UDMCs and participation in disaster preparedness training. This training appeared to enhance the knowledge and capacity of the respondents. The spill-over effects from disaster preparedness appeared to bolster the resilience of at-risk households, assist them in avoiding disaster-induced damages, and maintain consumption stability in the wake of disasters.

텍스트마이닝을 활용한 부동산 시장 인식 변화 연구

This study compared and analyzed real estate market trends over the last six years using social big data generated on the web and social media. To this end, the applicability of social network analysis as a method of confirming and understanding public perception of the real estate market was confirmed. Big data were used for analysis, and because keyword network analysis was at the heart of the study, the author's patented “emotional information analysis method” was partially used. The analysis result reveals that from 2017 to 2019, keywords related to the rising phase of the real estate market were derived with high influence, and real estate policy keywords were ranked highly. Meanwhile, from 2020 to 2022, keywords related to the real estate market downturn had a high impact, and those related to real estate costs were ranked high. These research findings are a preliminary study that seeks an effective research methodology in understanding the past and present real estate markets. More specific policy research should be conducted using these research results.

Central Industries in the Ecuadorian Input–Output Network. An Application of Social Network Analysis

The present study identifies the most influential industries in Ecuador according to the Input–Output Table for 2019. For this purpose, four weighted centrality indices are used from Social Network Analysis: Degree Centrality, Closeness Centrality, Betweenness Centrality, and Alpha Centrality. The results suggest that wholesale and retail trade, transportation, and professional activities are the most influential in the Ecuadorian economy due to their high centrality in commercial relationships. According to Chenery and Watanabe's methodology, all are base sectors, that is, they mainly are supply-driven. So, these centrality measures highlight the relevance of sectors based on their forward linkages. This result is not surprising. The theory of shock diffusion suggests that productive shocks on high centrality sectors can spread cascading effects downstream and generate aggregate fluctuations.

Communication Network Anaylsis of “WTS” (Want to Sell) on Twitter

This research examines communication networks and key actors in the distribution of information, types of relationships, and communication carried out via computer platforms using the keyword WTS (Want To Sell). This research aims to determine and analyze the communication network and actors involved in the phenomenon of the trending topic "wts (want to sell)". This research uses Computer Mediated Communication (CMC) Theory. Social Network Analysis (SNA) is a visualization method for understanding relationships between individuals, which includes a general overview of social networks with the topics discussed. Application of SNA with Netlytic and Gephi models. Data of 10,000 samples was successfully recalled by Netlytic among active Twitter users. This research uses quantitative research methods with a descriptive approach. The results of the research show that there is degree centrality on the @Indomyfess account, this account is the most dominant actor and has a score of 273, betweenness centrality on the @sunrowf account with a value of 1373.83 as the strongest account in distributing link-related information, closeness centrality is 2,736 nodes which are popular actor in information distribution, and eigenvector centrality on the @indomyfess account with a value of 1.0 as the most important actor in information distribution and continues to be associated with related information. Interaction between actors in the "WTS" communication network occurs because they exchange information about topics surrounding the keyword "WTS."

APPLICATIONS OF SOCIAL NETWORK ANALYSIS IN PROMOTING CIRCULAR ECONOMY: A LITERATURE REVIEW

Circular economy (CE) is a sustainable alternative to tackle global challenges like climate change, waste, and pollution. The relations, perceptions and behaviors of stakeholders in circular economic activities may form barriers that hinder the circular transition. The promotion of CE requires investigating the interactions and information flow between CE stakeholders from a network perspective. This study revisits the applications of social network analysis (SNA) in promoting CE. Related concepts of CE and the research contents of SNA are reviewed. A bibliometric analysis is conducted to provide a bird’s eye on the research status and trend. On this basis, we summarize the challenges of promoting CE and refine specific problems, around which we review the research status of network modeling methods and statistical measures, information diffusion models, mining methods of perceptions, and social influence analysis. This study outlines the pathways through which SNA contributes to promoting CE, such as through revealing the relational structure and characteristics of stakeholders, forming and changing perceptions of stakeholders, improving behaviors of stakeholders, and examining the development of CE. The lessons learned from the review and future prospects are extensively discussed in combination with the features of the information age from both theoretical and practical perspectives.

Social Network Analysis as a Tool in the Care and Wellbeing of Zoo Animals: A Case Study of a Family Group of Black Lemurs (Eulemur macaco).

Social network analysis (SNA) is an increasingly utilised technique in the literature examining the social structures and organisation of animals and understanding the bonds between groups and individuals. Using a case study as an illustration, the applications of SNA are explored, including the identification of dominance hierarchies and detection of sources of social pressure, with a particular focus on the applications of SNA to holistic assessments of animal welfare alongside other methods. Based on the examination of social dynamics in a family group of four black lemurs (Eulemur macaco), a primate whose social organisation is characterised by patterns of female dominance, it is demonstrated that SNA can be used to examine the affiliative and agonistic interactions between individuals living in human care. SNA showed species-typical forms of female dominance that were largely directed towards the two males, characterised by the initiation of aggressive interactions and male submission. More intricate relationships and consistent social roles across networks were revealed through the examination of SNA. It is concluded that SNA has wide-ranging benefits in the assessment of effects of environmental changes, such as informing social management decisions, developing enrichment and intervention programs, and guiding overall improvements to the housing and care of individual animals. SNA, as part of an animal welfare toolbox, could, therefore, be a pivotal technique for modern animal welfare assessment that considers individual animals and their social lives. By sharing a case study of the technique in use, it is hoped that animal collections may adopt similar modern and evidence-based assessment methods.

Construction Site Layout Planning: A Social Network Analysis

Construction site layout planning (CSLP) is the strategic arrangement and planning of construction site spaces, which has an enormous impact on the success of any construction project. Over the past two decades, multiple planning models have been developed to generate layouts that maintain safety and productivity within the construction environment. Yet these models vary significantly with disparate assumptions, many of which remain unstated. This study harnesses social network analysis (SNA) as a means to convert data into knowledge. It applies SNA to shed light on CSLP, providing a comprehensive overview of the existing models, and illuminating the critical parameters that should be considered in layout planning. This analysis delves deep into past methodologies and sets the potential for forthcoming research investigations. This study aims to be a reference for readers and researchers venturing into the realm of CSLP. Numerous related records and studies from diverse databases and sources were reviewed and analyzed. Out of these, 70 articles were singled out, from which 14 pivotal parameters were distilled as the foundation for any CSLP framework. Through the application of SNA, gaps within the existing research domain and literature were pinpointed. The study findings demonstrate the growing interest in shifting to cutting-edge approaches in CSLP. However, the results show that the majority of these models in the literature fall short of sufficiently addressing realistic facility representation, noise effects, or the construction impact on the surrounding environment. Accordingly, this research illuminates these knowledge gaps. The findings of this review guide future research by sketching a broad outline for future optimization models and planning studies.

Comparing qualitative and quantitative text analysis methods in combination with document-based social network analysis to understand policy networks

AbstractThe literature that reflects on the application of Social Network Analysis (SNA) in combination with other methods is flourishing. However, there is a dearth of studies that compare qualitative and quantitative methods to complement structural SNA. This article addresses this gap by systematically discussing the advantages and disadvantages relating to the use of qualitative text analysis and interviewing as well as quantitative text mining and Natural Language Processing (NLP) techniques such as word frequency analysis, cluster analysis, topic modeling, and topic classification to understand policy networks. This method-oriented comparative study features two empirical studies that respectively examine the Employment Thematic Network, established under the aegis of the European Commission, and the intergovernmental cooperation network set up within the Bologna Process. The article compares and discusses the underlying research processes in terms of time, human resources, research resources, unobtrusiveness, and effectiveness toward the goal of telling meaningful stories about the examined networks in light of specific guiding hypotheses. In doing so, the paper nurtures the debate on mixed-methods research on social networks amidst the well-known paradigm war between qualitative and quantitative methods in network analysis.

Methodological Exploration of Social Value Measurement in Mobile Network Services: Application of Social Network Analysis and System Dynamics Methodology

Methodological Exploration of Social Value Measurement in Mobile Network Services: Application of Social Network Analysis and System Dynamics Methodology

The Application of Social Network Analysis to Economic and Regional Development: Tertiary Educational Attainment by Sex and Nuts 2 Regions

Abstract Social Network Analysis (SNA) is a process of investigating the structure of a network. With the contribution of graph theory, it studies the relationships (links) that are established between different units, such as individuals, groups, or organizations (nodes), which are taken as interdependent rather than as autonomous. A social network can be compared to a web, which records the relationships or interactions of people. These networks include the social or professional interactions between units through which information and services are exchanged or contacts are recorded on a systematic basis. In this paper we attempt to introduce SNA in Economic and Regional Development, i.e. to examine the correlation of a regional education statistic indicator between certain regions. Specifically, we study the share of citizens who have completed higher education for the age group 25-64 years by gender and NUTS 2 regions. We present the visualisation of several networks, which are generated based on the degree of correlation of the data, and compute measures of centrality to compare the level of higher education by gender. The aim is to determine whether the application of Social Network Analysis could be an alternative research method in the study of Economic and Regional Development.

Lending a Hand: An Application of Social Network Analysis on Employee Resilience in Organizations

Lending a Hand: An Application of Social Network Analysis on Employee Resilience in Organizations

Efficient and Near-optimal Algorithms for Sampling Small Connected Subgraphs

We study the following problem: Given an integer k ≥ 3 and a simple graph G , sample a connected induced k -vertex subgraph of G uniformly at random. This is a fundamental graph mining primitive with applications in social network analysis, bioinformatics, and more. Surprisingly, no efficient algorithm is known for uniform sampling; the only somewhat efficient algorithms available yield samples that are only approximately uniform, with running times that are unclear or suboptimal. In this work, we provide: (i) a near-optimal mixing time bound for a well-known random walk technique, (ii) the first efficient algorithm for truly uniform graphlet sampling, and (iii) the first sublinear-time algorithm for ε-uniform graphlet sampling.