Social Network Analysis Research Articles

Social network analysis of regional transport carbon emissions in China: Based on motif analysis and exponential random graph model

Given the significant impact of transportation-related carbon emissions on air quality and climate change, understanding the regional dynamics of these emissions is crucial. Despite numerous studies on carbon emissions, there is a lack of comprehensive analysis of China's interprovincial transport carbon emission correlation network. Based on China's provincial data from 2007 to 2021, we analyzed the network's basic structural characteristics and categorized it into four significant plates to investigate their interactions. Subsequently, motif analysis is employed to examine the micro-correlation patterns within the network, and the Exponential random graph model (ERGM) is utilized to analyze the network's formation mechanism. Findings reveal that: (1) Provinces with high correlation intensity are predominantly concentrated in the eastern region, such as Shanghai and Beijing. Additionally, provinces in the eastern region assume a central role in the transport carbon emission correlation network, mainly receiving carbon emissions from other provinces. In contrast, the western region primarily emits carbon emissions to other provinces, continuously converging towards the center. (2) The network is segmented into net beneficiary plate, net overflow plate, bidirectional spillover plate, and broker plate, with distinct roles and influences across different years. (3) Bidirectional correlation motif structures emerge as primary influencers within the network, although specific structures impede interregional communication and collaborative emission reduction. (4) Internal network's structural variables, such as mutuality, cyclic triple, and geometrically weighted edgewise shared partner, along with influencing factors including government intervention, urbanization rate, openness, fiscal expenditure on transport, and province adjacency significantly impact the formation of the transport carbon emission correlation network. The above transportation network research provides a theoretical basis for the country to promote low-carbon transportation and improve air quality, and also has important guiding significance for the cross-regional collaborative emission reduction work of provinces.

Informal participation in digital planning: How can third parties use social media to shift power relations in planning?

In recent years, social media has become an influential tool for engaging various participants and facilitating inclusivity in digital planning. While many studies highlight local governments' use of social media for formal participation, limited research assesses its impact on power dynamics in informal participation. This study aims to fill the gap by identifying key features of social media that facilitate informal participation and applying Castells' four forms of network power to understand power dynamics among civil society, journalism, citizens, and governments in planning processes. It also develops a novel mixed-methods approach that combines social media scraping, social network analysis (SNA), semi-structured interviews, and field observation. This approach is applied to investigate the Enning Road regeneration project in Guangzhou as a case study. Analyzing data from China's Weibo, the study reveals network disputes across three dimensions: graph, community, and network statistics. Hyperlink-Induced Topic Search (HITS) and community detection results suggest that civil society and journalism have substantial networked power as they strategically utilize social media to promote collaboration, mobilize citizens, and foster communities. They also excise network-making power by switching online and offline networks, thereby transmitting online debate to a wide range of audiences and compelling local governments to shift planning priorities from demolitions to preservation.

A Social Network Analysis on Abu Sayyaf Kidnappings

The Abu Sayyaf Group (ASG) is a Philippine-based extremist organization globally known for engaging in terrorism, kidnappings, and various criminal activities. This study sought to comprehensively analyze Abu Sayyaf ’s kidnapping incidents using social network analysis to better understand their operational structures, patterns, and dynamics. The researchers applied centrality measurements to assess the significance of nodes in the network, along with the GirvanNewman algorithm for community detection to identify groups sharing similar characteristics and extract groups for various purposes. The findings revealed one kidnapping node to have the highest centrality score and holding the most significant influence and numerous incoming connections within the network. Another kidnapper node in the analysis of out-degree centrality displayed a proactive role, initiating multiple connections and shaping the network’s dynamic. The community detection uncovered seven distinct communities within the network, each demonstrating unique patterns and characteristics. The findings imply that kidnappings were conducted through groups rather than individuals. The majority of networks involving ASG members participating in multiple kidnapping events hold significant implications for national security strategies.

Characterizing an online, science-based affinity space using topic modelling, diversity indices, and social network analysis

Characterizing an online, science-based affinity space using topic modelling, diversity indices, and social network analysis

Are farmer organizations effective intermediaries and facilitators of agricultural innovations processes? Evidence from Tunisia

Integration of productivity, resource management, and institutional innovations is crucial across different system levels. Traditional research and extension services face challenges in disseminating innovations effectively, leading to the emergence of the agricultural innovation system (AIS) approach. AIS involves collaboration among various stakeholders to improve the technological, managerial, and institutional aspects of agriculture. Intermediary actors play a pivotal role in facilitating innovation exchange and learning processes. In Tunisia, farmer organizations (FOs) such as Groupements de Développement Agricole (GDAs) and Sociétés Mutuelles de Services Agricoles (SMSAs) are key intermediaries. However, there is a lack of comprehensive assessments of FOs' innovation roles, which this study aims to address using the social network analysis (SNA) method. The findings aim to shed light on FOs' effectiveness as innovation intermediaries and identify potential challenges and benefits associated with their roles in innovation processes through the comparison of three farmers' groups divided by their membership status to detect the effect of FO presence or absence on innovation facilitation and actor's configuration. The studied sample involves 592 farmers from the Kef and Siliana governorates in northwestern Tunisia. The results show that FOs are playing a crucial role in facilitating collaboration and knowledge exchange among farmers and other agricultural actors. However, their effectiveness varies depending on membership status and is always contextually influenced. While FOs contribute to reshaping sales channels and optimizing access to services, they face limitations in promoting inclusivity and equitable knowledge sharing. The recommendations include strengthening FOs' social dimension, addressing trust barriers, and investing in reinforcement measures, especially in human capital, to enhance their functionality. Strengthening measures can take many forms, such as facilitating regulation and investing in capacity building. Long-term strategies should re-evaluate the effectiveness of collective action and focus on fostering a culture of shared learning and local empowerment.

Multi-view attributed graph clustering based on graph diffusion convolution with adaptive fusion

Graph clustering is one of the popular techniques in data mining, which is widely used in social network analysis, recommendation system and anomaly detection. The existing methods based on multi-view attributed graph ignore the difference of different views and the consistency between views. To this end, this paper proposes a novel method for multi-view attributed graph clustering based on graph diffusion convolution with adaptive fusion. Specifically, a graph diffusion convolution is firstly employed to propagate features of the adjacency and attribute matrices on each view. A linear encoder–decoder is then used to learn the graph embedding representation of each view. Meanwhile, the consistency learning module is applied to capture the geometric relationship consistency of different views for finding a consistent embedding space during the embedding representation learning. To measure the importance of different views, the attention mechanism is subsequently utilized to adaptively learn the weights of different views to fuse multiple views of data for the final clustering. Finally, the graph representation learning and clustering tasks are jointly optimized to obtain a clustering friendly graph representation. Experiments conducted on two public datasets and three real futures datasets from the Zhengzhou Commodity Exchange demonstrate the effectiveness of the proposed method in the task of graph clustering.

#FrauenSagenNein - bridging the divide: analyzing the affective network of gender-critical alliances

ABSTRACT The influence of anti-gender movements is on the rise globally as they seek to prevent the expansion of transgender rights. In Germany, the anti-gender debate has gained renewed intensity in the wake of the publication of the draft Self-Determination Act in June 2022. Within this discourse, two prominent groups of actors emerged: gender-critical feminists and right-wing populists. Despite their divergent ideological and political positions, the affordances of social media provide them with a platform for collaborative interactions. Thus, under issue-related hashtags, the boundaries between these actors are becoming increasingly blurred. This research utilizes affect theory and analysis of hashtag activism to investigate the collective protest on Twitter against the proposed Self-Determination Act. Employing social network analysis (SNA), the study identifies pivotal structures and actors within the #FrauenSagenNein (#WomenSayNo) network. Additionally, qualitative content analysis delves into the narratives and sentiments employed in constructing hostile portrayals of transgender individuals. The findings illustrate that #FrauenSagenNein served as a bridging tool that connects gender-critical feminists and right-wing populist actors, leading to their intersection. Based on these findings, it is vital to critically monitor such alliances to counter the spread of binary gender norms and heteronormative beliefs.

Supplemental Material for Understanding the Developmental Transition Between Parallel and Social Play During Preschool: A Multiplex Social Network Analysis

Supplemental Material for Understanding the Developmental Transition Between Parallel and Social Play During Preschool: A Multiplex Social Network Analysis

How Do Virtual Communities of Practice Enhance Professional Connections and Social Capital?

Communities of Practice (CoP) were created to up-skill educators and to mitigate the disruption to physical therapist assistant (PTA) education because of the COVID-19 pandemic. Understanding CoP involves considering individuals and their interactions, making this project significant for pioneering CoP among PTA educators, and using social network analysis (SNA). The research question for this mixed-methods concurrent triangulation study was "To what extent did the network structure of virtual CoP reflect PTA educators' perceptions of participation and mentorship?" Forty of 60 CoP members participated in this study. We analyzed 8 virtual PTA educator CoP that met over 4 months. Study participants completed a questionnaire about their experiences, engagement, and network connections. Individual and group-level friendship and mentorship network measures were studied using SNA. Qualitative responses were analyzed using the case study design approach. All results were integrated to draw out the complexity of the PTA educator CoP. There was high engagement, with 97.5% (n = 39) reporting they would participate again, and 80% (n = 32) resolved an immediate issue affecting their role. Moreover, 92.5% (n = 37) reported a perception of encouragement with the environment. Study participants reported being mentored by an average of 1.2 individuals (median 0, range 0-5) and serving as mentors to an average of 1.4 individuals (median 0, range 0-7). Two themes, unity and knowledge, emerged through the qualitative analysis. Participation by PTA educators in CoP, whether focused on administration, teaching methods, or clinical education, brings valuable outcomes. Our research supports existing literature in physical therapy education. We discovered that participant engagement, nurturing mentor relationships, encouraging knowledge sharing, and promoting a sense of unity among educators are all important factors.

Hydroxyl-Amide Alkaloids from Pepper Roots: Potential Sources of Natural Antioxidants and Tyrosinase Inhibitors.

Pepper (Piper nigrum L.) is a widely used spice plant known for its fruits and roots, which serve as flavor enhancers in culinary applications and hold significant economic value. Despite the popularity of pepper fruits, their roots remain relatively understudied, with limited research conducted on their bioactive components. This study focused on discovering and separating the primary bioactive amide alkaloids found in pepper roots. The process involved using the antioxidant activity of crude fractions and the Global Natural Products Social Molecular Networking analysis platform. The process led to the discovery of 23 previously unknown hydroxyl-amide alkaloids. Notably, compounds 11, 12, and 14 showed excellent antioxidant activity, while compound 11 exhibited significant inhibitory effects on mushroom tyrosinase. Theoretical exploration of enzyme-ligand interactions was conducted through molecular docking and molecular dynamics simulation. The findings of this study highlight the potential of hydroxyl-amide alkaloids as antioxidant products and natural food preservatives in the pharmaceutical and food cosmetic industries.

Spatial spillover effects and correlation network analysis of green construction development efficiency in China

Green construction has been implemented globally to address resource depletion and environmental degradation. The effectiveness of green construction development depends on the efficiency of resource utilization and the common progress of all regions. However, existing studies have paid limited attention to green construction development efficiency (GCDE) and its spatial correlation. This study uses the super-efficiency slacks-based measure (Super-SBM) model, spatial econometric models, and social network analysis (SNA) method to reveal the spatial distribution, spatial spillover effects, and spatial correlation of GCDE across 30 provinces in China. The results indicate significant disparities in GCDE among regions, with the eastern region exhibiting considerably higher GCDE than the western and northeastern regions. There is evidence of spatial spillover effects, and the spatial correlation of GCDE is strengthening. Provinces with high GCDE occupy central positions in the GCDE network and exert spillover effects on other provinces, while underdeveloped provinces have opportunities to benefit from regional cooperation. The findings provide policy insights for the implementation and enhancement of green construction development actions. Moreover, they provide Chinese experience for green construction practices in other areas of the world.

Measuring the adaptive capacity of rangeland users under drought stress in North-easternIran: application of social network analysis

Drought events have significant impact on ecosystems and the livelihoods of rural communities in Iran. So, the purpose of this study was the analysis and evaluation of dimensions of adaptive capacity (AC) against drought in Bajestan county, Khorasan Razavi province, Iran. A questionnaire was used to collect data for social network analysis (SNA) and AC by the full network method. Then, a combined SNA-SEM model was developed to determine what was the social response of rangeland users in dealing with drought. In this study, the AC of rangeland users was compared in two groups of villages covered by the collaborative management or Carbon Sequestration Project (CSP) and uncovered by CSP. The results confirmed that the villages under CSP had higher levels of AC and social capital. Among five capitals, indicators of economic resources and information, skills and management were more effective on AC. Among the SNA indexes, effsize, constrain, indirects, density and betweenes centrality are more effective on AC of rangeland users. Results showed that identifying the key actors, who have the power and influence to determine the information flow and financial inflows in these components, can help increase the AC of rangeland users to mitigate effects of drought.

The role of teaching presence in students’ behavioral engagement

This study explores the role of teaching presence in students’ behavioral engagement in online learning. We analyzed three classes conducted by one teacher over a period of three semesters. Content analysis and social network analysis were employed to analyze the teacher’s teaching presence and students’ behavioral engagement. Results indicated that teaching presence promoted students’ interaction and reduced the number of marginal students. Results also showed that design and organization, facilitating discourse, and technological support positively contributed to students’ behavioral engagement in their learning, while direct instruction and assessment had some inhibitory roles in students’ behavioral engagement. This study adds to the literature on how teaching presence indicators influence students’ behavioral engagement.

Artificial Intelligence Model for Detecting Tax Evasion Involving Complex Network Schemes

Tax evasion through complex network schemes poses a significant challenge to tax authorities, leading to substantial revenue losses. This paper aims to develop and evaluate an artificial intelligence model designed to detect tax evasion within complex corporate networks, providing a comprehensive overview and prediction of tax avoidance behaviors. Employing a systematic literature review and document analysis of applicable tax regulations, the study utilizes Social Network Analysis (SNA) as a primary technique for mapping and analyzing taxpayer networks. The process involves matching taxable identities, constructing taxpayer graphs, extracting features, and developing a machine learning model. The proposed architectures and processes demonstrate the potential for tax authorities to enhance their capabilities in detecting tax evasion involving complex networks, with the machine learning model effectively identifying features related to both individual and network characteristics of taxpayers. The findings suggest that the integration of artificial intelligence and big data analytics can significantly improve the detection of tax evasion in complex corporate structures, offering valuable tools for tax authorities to better enforce tax compliance.

Spatial Correlation of Land Use Carbon Budget Based on Social Network Analysis： A Case Study of Chongqing Metropolitan Area

Clarifying the temporal and spatial changes in the carbon budget in the Chongqing metropolitan area and exploring the spatial correlation of land use carbon budget are of great significance for realizing the regional "double carbon" goal. Using 21 districts and counties in Chongqing metropolitan area as the research scale, the IPCC inventory method, carbon emission coefficient method, Gini coefficient, gravity model, and social network analysis were used to estimate the net carbon emissions from land use in Chongqing metropolitan area from 2000 to 2020, and the spatial correlation of the carbon budget was obtained. The results revealed that： ① In the past 20 years, the carbon budget of the Chongqing metropolitan area showed an overall upward trend, with an average annual growth rate of 2.83%, and the spatial distribution was "higher in the north and south, higher in the east and west, and lower in the middle." ② During the past 20 years, the spatial difference of net carbon emissions in the Chongqing metropolitan area became highly average, and the overall Gini coefficient decreased by 11.42%, whereas the intra-group difference was the largest in key development zones. ③ In the past 20 years, the overall structure of the spatial correlation network of land use carbon budget in the Chongqing metropolitan area has become stable and complex, and the network density and network correlation number have increased by 0.43 and 180, respectively, the network correlation degree has increased to 1, and the network health degree has improved. ④ In the individual network structure of land use carbon budget in the Chongqing metropolitan area, the degree centrality of each district and county has increased, and the increase in the central urban area was the most significant, with an increase of 81, whereas the decline in intermediate centrality and proximity centrality has promoted the regional coordinated development and integration process in the metropolitan area. ⑤ In the past 20 years, the density of net carbon emissions in the Chongqing metropolitan area has increased as a whole, with the density of the core area increasing by 0.35 and the density of the core-edge area increasing by 0.34. By exploring the change and spatial correlation of land use carbon budget in the Chongqing metropolitan area, this study clarified the spatial distribution difference of carbon budget and provided support for regional green development.

How do partner selection strategies affect the amount of funding in collaborative research projects? Evidence using the dual-projection approach

The selection of partners plays a crucial role in determining the success of collaborative projects. This study makes a valuable contribution to the existing literature on collaborative research and development by examining the impact of different partner selection strategies on the funding received in collaborative research projects. Previous studies have not shown sufficiently how collaborating with new partners as opposed to existing ones influences the amount of funding received. To bridge this gap, we use the innovative dual-projection approach from Social Network Analysis. Specifically, we analyze the network structure of projects funded by Horizon 2020, the eighth European Framework Programme. Our findings show that compared to collaborating with established partners, collaborating with new ones increases the likelihood of securing more funding when entering a new project. Moreover, projects coordinated by private or public organizations rather than research centers and higher education institutions have a higher probability of obtaining greater funding. However, the significance of partner connections diminishes with decreasing proximity to the focal organization. Ultimately, our results offer valuable insights into the effectiveness of the European Research and Technological Development Policy in fostering excellent science through cross-collaboration among a diverse group of actors.

Spatial correlation network and influencing factors analysis of supply efficiency of basic public service (SEBPS) in rust belt regions of China: An empirical study from Northeast China

Equalizing basic public service is crucial for achieving social fairness and justice. Promoting the improvement of public service can be achieved through the improvement of supply efficiency and cross-regional coordinated development. We employed the Super-SBM model, modified gravity model, and social network analysis method to empirically study the spatial correlation network characteristics of supply efficiency of basic public service (SEBPS) based on the data of 34 prefecture-level cities in the three provinces of Northeastern China from 2011 to 2020. The results revealed the overall efficiency of SEBPS in the study area is relatively low, and the supply efficiency is polarized. There is significant non-equilibrium between regions, and the regional difference moment gradually expands over time. There exists a strong spatial correlation between SEBPS in each city, and the overall correlation effect is weak but the network stability has improved. Each city had different roles and statuses in the spatial correlation network, creating a hierarchical structure. The level of geospatial proximity had a positive impact on the spatial correlation and spillover of the network. The level of economic development and infrastructure configuration hurt the network, and there is obvious heterogeneity between cities. The differences in industrial structure, opening up level, urbanization level, population density, the level of fiscal decentralization and technological innovation and personal income level among cities affected the SEBPS network. This study not only provides a new network research perspective and theoretical foundation for public service policies in China's rust belt region but also serves as a reference for urban development in similar regions.

Exploring Electric Vehicle Patent Trends through Technology Life Cycle and Social Network Analysis

In response to environmental and energy challenges, electric vehicles (EVs) have re-emerged as a viable alternative to internal combustion engines. However, existing research lacks a comprehensive analysis of the technology life cycle of EVs in both global and South Korean contexts and offers limited strategic guidance. This study introduces a novel approach to address these gaps by integrating the S-curve model with social network analysis (SNA), time series analysis, and core applicant layouts. The study specifically utilizes the logistic curve to model technology growth. It applies SNA methods, including International Patent Classification (IPC) co-occurrence analysis and the betweenness centrality metric, to identify the stages of technological development and sustainable research directions for EVs. By analyzing patent data from 2004 to 2023, the study reveals that EV technologies have reached the saturation phase globally and in South Korea, with South Korea maintaining a two-year technological advantage. The research identifies sustainable research directions, including fast charging technology and charging infrastructure, battery monitoring and management, and artificial intelligence (AI) applications. Additionally, the study also determined the sustainability of these research directions by examining the sustainability challenges faced by EVs. These insights offer a clear view of EV technology trends and future directions, guiding stakeholders.

Educating gender equity through non-competitive cooperative motor games: Transforming stereotypes and socio-affective dynamics

Non-competitive cooperative motor games hold the potential to educate gender equity from a relational perspective. These motor games present educational relational challenges where different genders interact for mutual benefit. Such interactions generate emotional well-being, enhance socio-affective relationships, and promote feminized motor conducts that potentially perform gender hegemonies and reduce gender stereotypes. This study, grounded in the ethics of care and motor praxeology, explored the effects of non-competitive cooperative motor games on group and cross-gender socio-affective rejections, gender stereotypes and emotional well-being, along with the mediation effects of these interconnected variables. Researchers conducted a quasi-experimental study where 11 participants (Mage = 11.16; SD = 1.35) from a summer sports camp played non-competitive cooperative motor games. The study employed sociometric questionnaires, emotional questionnaires (PANAS-C) and gender stereotype questionnaires (NATGEN). Data were analyzed using inferential and descriptive statistical techniques, social network analysis and mediation analysis. Results revealed the effectiveness of non-competitive cooperative motor games in reducing cross-gender rejections among girls, maintaining low group rejections, promoting emotional well-being and reducing gender stereotypes. Mediation analysis showed that gender stereotypes and positive emotions act as a protective barrier against group rejection. This study presents an innovative educational perspective that combines the scientific foundations of motor praxeology with the theoretical bases of the ethics of care. The research provides teachers and researchers with a practical pedagogical tool based on non-competitive cooperative motor games. These games are designed to enhance gender equity, promote emotional well-being, and reduce gender-based rejection and stereotypes. Keywords: sociometry, group cohesion, non-formal education, friendship, feminism, sport, inclusion, social acceptance

Corporate board network and green technological innovation: a view of guanxi culture in China

ABSTRACT Given the pivotal role of guanxi as an informal arrangement for resource acquisition in China, this study integrates social capital theory with social network analysis to explore the effect of firms’ guanxi in board networks on green technological innovation (GTI). The findings suggest that firms with more stable and flexible guanxi are likely to have access to more adequate and diverse social capital to support GTI. Furthermore, we theorise that these relationships are more pronounced for firms with better corporate social responsibility performance, as this may strengthen connected firms’ trust in the focal firm and allow it to draw more GTI resources. This effect, however, only holds for non-state-owned firms. Stronger absorptive capacity also enhances the contribution of guanxi to GTI by helping firms gain more recognition from connected firms for GTI engagement, thereby positively moderating the relationships. This study may provide insights for shareholders to promote firms’ GTI.