Social Network Analysis Research Articles

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.

Accelerating the sustainability transition of brown regions: Unlocking the speed factor

Green innovations aim to improve and reduce the environmental impact of economic activities. Thus far, research focus on the positive trajectories of green transition. Continuing in this direction we focus on brown regions (i.e. specialized in fossil-fuel technologies) and the challenges that they face to become sustainable. Taking as example German Labour Market Regions we identify brown regions and measure their transition using an innovative approach based on Social Network Analysis and Knowledge Spaces. We find that the technological relatedness to green technologies, having a high life satisfaction in the region and the presence of the green party in the local government facilitate the transition. However, strong economic structures based on brown activities do not generally increase the transition speed. Our findings imply that technological and socio-economic characteristics influence the speed of transition, a dimension not yet accounted for in the sustainability transition literature.

COVID-19 Impacts on the Spatial Network of City Tourism in Fujian Province, China

This study examined the tourism spatial distribution of nine cities in the Fujian province and assessed the impacts of COVID-19. The modified gravity model found that it was widely dispersed, with uneven and relatively independent tourism development in different cities. The social network analysis showed that tourism connections across cities were significantly reduced after the pandemic. The impacts of brand awareness and transport accessibility on spatial networks were positive in the pre-pandemic period but became negative during the pandemic. In contrast, tourist volume had negative impacts on spatial networks pre-pandemic but had positive ones during the pandemic. Tourism resources and market performance had significantly positive impacts in the post-pandemic era. These findings provide advice on tourism recovery and destination management in coping with future critical events. In the spatial distribution network of the tourism economy in Fujian Province, cities have different positions and roles, so development strategies should be differentiated according to the characteristics of each city. For example, more supportive policies should be introduced to help the tourism development of cities with disadvantages. It also contributes to the theoretical gravity framework in tourism and the research scope of the social networks analysis at the city level.

Social network based link correlation using graph neural network with deep learning architectures for feature vectors prediction and classification

SummaryIn recent years, social network analysis has received a lot of interest. A critical area of research in this field is link prediction. Link prediction is researched for other forms of social networks. Still, because social link networks (SLNs) change over time and depend on the discussed topics, this network has unique difficulties. Recent studies have focused on three main issues: extending link prediction to a dynamic environment, forecasting formation, and destroying network linkages that change over time. Although it is a challenging issue, deep learning (DL) techniques have been demonstrated to increase prediction accuracy significantly. This research proposes a novel approach to link correlation for social networks based on DL architectures in feature vector prediction and classification. Here the input data has been processed for smoothening and normalization with noise removal. Then, the feature vector was predicted using a dynamically structured convolutional radial basis neural network for this data. The expected feature vector has been classified using a stochastic gradient‐based graph neural network. The experimental analysis is carried out for various social network data in terms of accuracy of 98%, precision of 85%, recall of 86%, F‐1 score of 75%, AUC of 72%, and RMSE of 76%.

Virtual community engagement: Engaging virtual communities for nation branding through sports

Major sporting events bring a range of social, economic, and reputational benefits for hosting nations and serve as a vehicle to showcase the nation brand. Social media engagement during sport events provides opportunities for community members to cocreate, curate and communicate their nation brand’s attributes. While research in nation branding and community engagement has examined the ways place-based communities contribute to nation branding, little is known about how and in what ways virtual communities contribute to their nation brand. This study addresses this gap by examining the virtual brand community (VBC) surrounding the 2022 Beijing Winter Olympics. Using social network analysis and content analysis, the findings suggest that VBC contributions is dependent on the composition, participation, and cultivation of conversations focusing on the personality traits of a nation brand. Theoretically, the outcomes from this study advance understanding of leveraging the properties of VBCs in making contribution to the production and maintenance of an authentic nation brand, that values community perspectives facilitated through community engagement. For public relations, it extends the current research in digital social-level engagement in virtual settings and outlines future research in participatory approaches to nation branding. The growing use of major sporting events in supporting nation branding goals, particularly in Asian and African continents, suggests the outcome of this study has implications at regional and international levels to guide public relations managers in major events to facilitate sustainable VBCs for authentic place branding outcomes.

Research on the Correlation of Safety Risk of Railway Bridge Construction Based on Meta-Analysis

China has emerged as a prominent global player in the field of railways, with numerous railway construction projects spanning across diverse locations. Railway bridges, as a crucial component of railway construction, warrant significant attention. Meta-analysis, a statistical method that systematically synthesizes research findings, has been utilized to summarize and compare the results of safety risk management studies pertaining to railway bridge construction in China. By integrating social network analysis and evidence-based assessment of the literature, this study explores the interrelationships among risk factors. Within a specific railway bridge construction project, various safety risk factors may originate from common sources, including environmental factors, material and equipment factors, technical factors, management factors, personnel factors, and bridge-specific factors. Notably, there exists coupling among these security risk factors, whereby the presence or occurrence of one factor can influence the probability or severity of consequences associated with other factors. The results reveal that safety risk factors in railway bridge construction accumulate and propagate, thereby impacting the efficacy of safety risk management. Moreover, these factors are significantly influenced by the complexities inherent in the geo-meteorological and social-technical systems. This finding provides valuable insights for innovations in security risk management practices and offers suggestions for future innovation pathways.

Youth political participation and digital movement in Indonesia: the case of #ReformasiDikorupsi and #TolakOmnibusLaw

The younger generations have always been at the forefront of and contributed significantly to social movements that are critical of state policies or respond to socio-political situations. This is also what happened in large movements involving young people in Indonesia, conventionally in the form of demonstrations and digital movements. This study seeks to explore the relationship between youth political participation, digital media, and the development of youth political identity by analyzing young people’s engagement in some of the most significant political protests in Indonesia during the mass protests of #ReformasiDikorupsi, a movement opposing the amendments of several important bills, and #TolakOmnibusLaw, a movement opposing the establishment of a new single bill related to labor and investment, between 2019 and 2020. By analyzing quantitative data through data mining from Twitter from October 2019 to November 2020 with the social network analysis method, this study discusses online youth participation in social movements and the way they shape their political identity. Our research found that young people play an active and central role in building online and offline movements. In particular, the online movement contributes to the growth of the offline protest movement, and vice versa, because of concerns about the practice of illiberal democracy.

Impacts of Brazilian Green Coffee Production and Its Logistical Corridors on the International Coffee Market

Background: The coffee industry is one of the most important world supply chains, with an estimated consumption of two billion cups daily, making it the most consumed beverage worldwide. Coffee beans are primarily grown in tropical countries, with Brazil accounting for almost 50% of the production. The objective of this study is to examine the Brazilian trade between 2018 and 2022, focusing on state producers, logistical corridors, and importer countries. Methods: The methodology approach revolves around a quantitative method using Social Network Analysis measures. Results: The results reveal a massive concentration in local production (99.5%—Minas Gerais), port movements (99.9%—Santos, Itaguai, and Rio de Janeiro), and country buyers (80.9%—the United States, United Kingdon, and Japan). Conclusions: The study concludes that the Brazilian green coffee supply chain relies on a fragile and overloaded logistical network. Due to that, this study indicates that the stakeholders and decision-makers involved must consider this high concentration of production in some areas and companies. They must also address the bottlenecks in logistical corridors and the fierce competition involved in acquiring and processing Brazilian coffee production because these factors can drastically affect the revenue of the companies operating in this sector.

Trading with Pariahs: North Korean Sanctions and the Challenge of Weaponized Interdependence

Abstract Why has North Korea avoided capitulating to economic sanctions pressures? We argue that economic sanctions have been ineffective and unsuccessful for three reasons: North Korea’s adeptness at evasion, other states’ unwillingness to enforce economic sanctions, and North Korea’s efforts to avoid panopticon effects via its diplomatic statecraft and policies that allow states to monitor and collect information on others. We contend that the implementation and enforcement of economic sanctions led North Korea to alter its trading network, reallocating trade to countries (or nodes) within its network willing to ignore international sanctions. As a result, these network shifts limited the ability of senders to weaponize the interdependence between them and North Korea effectively. We further highlight how North Korea conducts diplomatic statecraft in such a way that limits the impact of sanctions enforcing states from effectively monitoring it and limiting the impact of panopticon effects senders generate. To test our theory, we employ an ordinary least squares regression followed by social network analysis to understand how North Korea’s network of trade has adapted over time in the face of punishing economic sanctions. We find that states with greater diplomatic engagement enjoy better centrality than sanctions enforcing states. Also, economic sanctions at critical junctures induce changes to North Korea’s network over time with commercial imperatives to trade replaced by a North Korean near reliance on China. We further demonstrate that economic sanctions likely induce changes to the structure of the network as North Korea seeks to insulate itself from the impact of economic sanctions.

Using social network analysis to investigate mathematical connections in U.S. and Chinese textbook problems

Textbooks, as potentially implemented curricula, play an important role in supporting classroom teaching and learning. Mathematical connections, one of the essential and hot topics advocated in mathematics education, have been emphasized in national curriculum reforms in various countries. However, little is known about the connection networks represented in school textbooks; even less has been done to compare textbooks from different countries. In this study, we propose an innovative method for examining how connections are represented in two popular U.S. (the UCSMP series) and Chinese (the PEP-A series) high school textbook problems involving quadratic relations. By using social network analysis, we identified 1129 connections, characterized connection networks into dense, moderate, and sparse digraphs, identified influential, prominent, and dual concepts and representations, and evaluated the strength between typical and reverse connections. The results revealed that the Chinese series presented a denser network of balanced between-concept connections but limited within-concept connections. The U.S. series exhibited more within-concept connections but emphasized typical connections, thus validating the potential of this innovative method. From this study, we suggest that our novel method provides a theoretical contribution to textbook analysis and connection analysis, which has rich implications for practice, for example, examining the network of connections students construct as a way to assess and to promote their conceptual understanding, and our approach opens the possibility of adopting new and efficient analytical tools from social network analysis in mathematics education research.

Analysis of the Spatial Correlation Network and Driving Mechanism of China’s Transportation Carbon Emission Intensity

From 2008 to 2021, this study analyzed the spatial correlation characteristics between provincial transportation carbon emission intensity and explored ways to reduce transportation carbon emissions. This study used the modified gravity model, social network analysis (SNA) method, and temporal exponential random graph model (TERGM) to analyze the spatial correlation network evolution characteristics and driving mechanism of China’s transportation carbon emission intensity. This study found that China’s transportation carbon emission intensity and spatial correlation network have unbalanced characteristics. The spatial correlation network of transportation carbon emission intensity revealed that Shanghai, Beijing, Tianjin, Guangdong, Fujian, and other provinces were at the center of the network, with significant intermediary effects. The spatial correlation of transportation carbon emission intensity was divided into four functional plates: “two-way spillover”, “net benefit”, “broker”, and “net spillover”. The “net benefit” plate was mainly located in developed regions, and the “net spillover” plate was primarily located in underdeveloped regions. Endogenous structural and exogenous mechanism variables were the main factors affecting the evolution of the spatial correlation network of provincial transportation carbon emission intensity.

Social Network Analysis of Ancient Japanese Obsidian Artifacts Reflecting Sampling Bias Reduction

Social Network Analysis of Ancient Japanese Obsidian Artifacts Reflecting Sampling Bias Reduction

Peer technical support in preservice teacher education: A mixed methods social network analysis and phenomenological study to understand relative expertise

Peer technical support in preservice teacher education: A mixed methods social network analysis and phenomenological study to understand relative expertise

Forecasting Future Research Trends in the Construction Engineering and Management Domain Using Machine Learning and Social Network Analysis

Construction Engineering and Management (CEM) is a broad domain with publications covering interrelated subdisciplines and considered a key source of knowledge sharing. Previous studies used scientometric methods to assess the current impact of CEM publications; however, there is a need to predict future citations of CEM publications to identify the expected high-impact trends in the future and guide new research efforts. To tackle this gap in the literature, the authors conducted a study using Machine Learning (ML) algorithms and Social Network Analysis (SNA) to predict CEM-related citation metrics. Using a dataset of 93,868 publications, the authors trained and tested two machine learning classification algorithms: Random Forest and XGBoost. Validation of the RF and XGBoost resulted in a balanced accuracy of 79.1% and 79.5%, respectively. Accordingly, XGBoost was selected. Testing of the XGBoost model revealed a balanced accuracy of 80.71%. Using SNA, it was found that while the top CEM subdisciplines in terms of the number of predicted impactful papers are “Project planning and design”, “Organizational issues”, and “Information technologies, robotics, and automation”; the lowest was “Legal and contractual issues”. This paper contributes to the body of knowledge by studying the citation level, strength, and interconnectivity between CEM subdisciplines as well as identifying areas more likely to result in highly cited publications.

Research on the Resilience Assessment of Cross-border E-commerce Supply Chains under the RCEP Agreement

Under the RCEP (Regional Comprehensive Economic Partnership) agreement, cross-border e-commerce supply chains face the multiple risks that can lead to supply chain disruptions, severely affecting a nation's economic security, trade stability, and international market competitiveness. Enhancing supply chain resilience is a key method to address supply chain disruptions. This study, based on an analysis of the characteristics of cross-border e-commerce supply chains, utilizes Grounded Theory and Social Network Analysis to construct an evaluation indicator system. The resilience indicator system for cross-border e-commerce supply chains under the RCEP agreement developed in this study aids in the efficient and stable development of businesses.

Optimized Multi-Scale Mixed Dense Graph Convolution Network for Career Impression Management in College Students Based on Social Network Analysis

This investigation attempts to increase success rate of employment, entrepreneurship by addressing the dangers that college students face when seeking work and starting their own businesses in rural areas. It offers a way of social environment analysis based on employment, entrepreneurship of college students in rural areas. This approach comprehends the employment and social environment of college students' rural employment, entrepreneurship as research goal. It thoroughly examines, detects component parts of the college students' rural employment, entrepreneurship environment. In this manuscript, Optimized Multi-Scale Mixed Dense Graph Convolution Network for Career Impression Management in College Students Based on Social Network Analysis (MSMDGCN-CIM-CS-SNA) is proposed. Initially input data are gathered from Xing social network Dataset. To execute this, input data is pre-processed using Distributed Adaptive Cubature Information Filtering(DACIF) and it removes the noise from collected data; then the Pre-processed data are fed to MSMDGCN for effectively categorize Career Impression Management in College Students. In general, MSMDGCN does not express adapting optimization strategies to determine optimal parameters to ensure accurate Career Impression Management in College Students. Hence, the Sand Cat swarms optimization(SCSOA) to optimize Quantum Conditional Generative Adversarial Network which accurately Career Impression Management in College Students based on Social Media.Then the proposed MSMDGCN-CIM-CS-SNA is implemented and the performance metrics like Student Origin; Proportion of Daily Living Expenses, Student’s Graduation Trend, Selection of Career Areas for College Students, Units of College Student’s Career Intention and Computation Time the World are analyzed. Performance of the MSMDGCN-CIM-CS-SNA approach attains 18.75%, 26.89% and 32.57% higher Student’s Graduation Trend; 16.87%, 24.57% and 32.94% lower Units of College Student’s Career Intention and 18.43%, 25.64% and 31.40% lower Computation Time when analyzed through existing techniques like analysis on social environment of college students’ rural employment with entrepreneurship (ASE-CS-REE), recommendation model for college career entrepreneurship projects depend on deep learning (RM-CCE-DL), group relationship mining of college students depend on predictive social network (GRM-CS-PSN) methods respectively.

Big Data Analysis and User Behavior Prediction of Social Networks Based on Artificial Neural Network

The prediction of user behavior in social networks is of great significance for understanding user dynamics, personalized recommendation, and information dissemination. With the development of artificial intelligence (AI) technology, especially the application of artificial neural networks in big data analysis, new solutions and technical means have been provided for the analysis of user behavior in social networks. This study constructs a social network user behavior prediction model based on artificial neural networks. The article first reviews related research, establishes a research framework, and then describes in detail the functional structure of the user behavior prediction system. The key data structure design is meticulously constructed and the model is built through steps such as data preprocessing module, 3D feature frame construction module, feature mapping module, and feature prediction module. In addition, the article also constructs a group behavior theme probability prediction model based on an improved encoder-decoder model, which further enhances the understanding of group behavior of users in social networks. Through comparative experiments, the model proposed in this paper demonstrates its effectiveness in predicting individual and group user behaviors. The experimental results show that the model can accurately predict the behavior patterns of users in social networks and is superior to existing methods in terms of prediction accuracy and computational efficiency. This research shows that artificial neural networks are a powerful tool for analyzing big data in social networks and for predicting user behavior. The success of the study verifies the effectiveness of the model, provides a new technical path for future analysis of user behavior in social networks, and is expected to be widely applied in areas such as personalized recommendation and information dissemination analysis.

An Application of Innovative Algorithm of Integrated Social Network Analysis with Statistical Lstm Chain Network Analysis (Slstm-Cna) for Entrepreneurial Team Member Selection

Selecting the right members for an entrepreneurial team is a critical step in ensuring the success and viability of a startup venture. Each team member brings unique skills, experiences, and perspectives that contribute to the overall vision and execution of the business idea. The factors such as cultural fit, adaptability, and a shared commitment to the venture's mission and values can foster cohesion and synergy within the team. This paper presents an innovative algorithm for the selection of entrepreneurial team members, Leveraging insights from social network dynamics and LSTM-based predictive modeling, the algorithm aims to identify individuals with the highest potential for contributing to the success of a startup venture. By analyzing social network structures, the algorithm identifies key influencers and connectors within professional networks, thereby facilitating the identification of candidates who possess valuable connections and collaborative capacities. Furthermore, SLSTM-CNA enables the algorithm to forecast future network trends and dynamics, aiding in the selection of team members who can adapt and thrive in evolving entrepreneurial ecosystems. simulated entrepreneurial ecosystem, the algorithm identified individuals with the highest potential for contributing to startup success based on various criteria, including network centrality, connectivity, and predictive network dynamics. For instance, individuals with a centrality score exceeding 0.7 demonstrated a 25% higher likelihood of facilitating valuable connections critical for business growth. Additionally, candidates identified through SLSTM-CNA exhibited a 30% increase in adaptability to changing network structures compared to those selected through conventional methods

When perceived similarity overrides demographic similarity: examining influences on STEM students’ developmental mentor networks

BackgroundWhile dyadic faculty–mentored relationship research currently saturates the mentoring literature, recent developments suggest the need for a broader consideration of a student's mentor network. Research taking a network approach may provide deeper insights into the formation and benefits of mentorship for undergraduate students in science, technology, engineering, and mathematics (STEM) disciplines. Utilizing Developmental Mentor Network Theory and ego-centric social network analysis, this pre-registered study evaluates how the characteristics of mentees and mentors relate to both the content of support and structure of mentor networks in a large sample of White and Hispanic/Latino(a) STEM undergraduates across 12 universities.ResultsResults were nuanced but showed that perceived psychological similarity with their mentor(s) predicted both dyadic and network average levels of mentor support (i.e., psychosocial, career, role modeling) and relational satisfaction. Furthermore, results point to homophily and engagement in undergraduate research effects on mentor network structures.ConclusionsThese findings highlight the importance of using a network approach to deepen our understanding of the factors (e.g., psychological similarity) that may influence the formation and maintenance of robust and diverse supportive mentoring networks.

The impact and management of internet-based public opinion dissemination during emergencies: A case study of Baidu News during the first wave of coronavirus disease 2019 (COVID-19).

The coronavirus disease 2019 (COVID-19) public health emergency has had a huge impact worldwide. We analyzed news headlines and keywords from the initial period of COVID-19, and explored the dissemination timeline of news related to the epidemic, and the impact of Internet-based media on the public using lifecycle theory and agenda-setting theory. We aimed to explore the impact of Baidu news headlines on public attention during the first wave of COVID-19, as well as the management mechanism of regulatory departments for social public opinion. We searched Baidu News using the keywords "Novel Coronavirus" and "COVID-19" from 8 January to 21 February 2020, a total of 45 days, and used Python V3.6 to extract news samples during the first wave of the epidemic. We used text analysis software to structurally process captured news topics and content summaries, applied VOSviewer V6.19 and Ucinet V6.0 to examine key aspects of the data. We analyzed the impact of Baidu News headlines on social opinion during the first wave of COVID-19 in the budding, spread, and outbreak stage of the information lifecycle. From clustering visualization and social network analysis perspectives, we explored the characteristics of Baidu News during the initial stage of the COVID-19. The results indicated that agenda-setting coverage through online media helped to mitigate the negative impact of COVID-19. The findings revealed that news reporting generated a high level of public attention toward a specific emergency event. The public requires accurate and objective information on the progress of COVID-19 through Baidu News headlines to inform their planning for the epidemic. Meanwhile, government can enhance the management mechanism of news dissemination, correct false and inaccurate news, and guide public opinion in a positive direction. In addition, timely official announcements on the progress of the COVID-19 outbreak and responses to matters of public concern can help calm tensions and maintain social stability.