Search Results (25)

This study posits the need for a conceptual multi-risk management approach for fresh produce, an essential product category for societal resilience and one constantly affected by climate change, policy volatility, and geopolitical disruptions. The research started with a literature-informed typological risk mapping, leading to Gephi ver 0.10.1 visualizations of networks related to this trade. Network analysis using 2024 bilateral trade data revealed a core–periphery topology, with the United States, Spain, and the Netherlands as central hubs. A gravity-based simulation model was, lastly, used to address the following question: what structural vulnerabilities and flow-based sensitivities define the global fresh produce trade, and how do they respond to simulated multi-risk disruptions? The model used the case of the USA as a global trade hub and induced two compounding risks: a protectionist tariff policy shock and a climate-related shock to its main supplier. The conclusion was that the fragility in the fresh produce trade enhances the cascading effects that any risk event may have across the environmental, economic, and social sustainability dimensions. This paper emphasizes the need for anticipatory governance, the diversification of trade partners, and investment in cold chain resilience, offering a means for policymakers to acknowledge the risk and mitigate the threats to the increasingly fragile fresh produce trade. Full article

This study investigates the dynamics of passenger satisfaction and sustainable urban mobility within the context of multimodal railway hubs, focusing on travel behaviors at major stations in China. Against the backdrop of rapid urbanization and the nation’s initiatives to improve transportation efficiency, this research employs social media data analysis to assess passenger sentiment across six key transportation hubs in Eastern China. Utilizing methodological approaches such as keyword frequency analysis and semantic categorization of 39,061 Dianping reviews, supplemented by network visualizations with Gephi, this study reveals insights into factors influencing passenger satisfaction beyond travel efficiency. Signage quality, facility availability, queueing, and crowding emerge as significant determinants of passenger behavior. The study underscores the importance of strategic improvements in station design, navigational aids, and facility management, grounded in real-time data analytics and passenger feedback, to enhance overall passenger satisfaction and promote sustainable urban mobility. This research contributes to advancing understanding of passenger behavior and informs efforts aimed at improving urban transportation systems to meet the evolving needs of passengers and cities. Full article

Over the last decade, there has been a large amount of research on technology-enhanced learning (TEL), including the exploration of sensor-based technologies. This research area has seen significant contributions from various conferences, including the European Conference on Technology-Enhanced Learning (EC-TEL). In this research, we present a comprehensive analysis that aims to identify and understand the evolving topics in the TEL area and their implications in defining the future of education. To achieve this, we use a novel methodology that combines a text-analytics-driven topic analysis and a social network analysis following an open science approach. We collected a comprehensive corpus of 477 papers from the last decade of the EC-TEL conference (including full and short papers), parsed them automatically, and used the extracted text to find the main topics and collaborative networks across papers. Our analysis focused on the following three main objectives: (1) Discovering the main topics of the conference based on paper keywords and topic modeling using the full text of the manuscripts. (2) Discovering the evolution of said topics over the last ten years of the conference. (3) Discovering how papers and authors from the conference have interacted over the years from a network perspective. Specifically, we used Python and PdfToText library to parse and extract the text and author keywords from the corpus. Moreover, we employed Gensim library Latent Dirichlet Allocation (LDA) topic modeling to discover the primary topics from the last decade. Finally, Gephi and Networkx libraries were used to create co-authorship and citation networks. Our findings provide valuable insights into the latest trends and developments in educational technology, underlining the critical role of sensor-driven technologies in leading innovation and shaping the future of this area. Full article

This study aimed to produce a network structure in Integrated Urban Farming Program in Bandung City to map the involved policy actors to realize a food-smart city. In this study, a mixed method was used with an exploratory sequential strategy involving policy actors from the government, private sector, academia, community, and mass media. To obtain a network structure in Integrated Urban Farming toward determining the most important actors, the Social Network Analysis (SNA) approach was also employed through the Gephi application. From this context, the structure emphasized four dimensions, namely Degree, Betweenness, Closeness, and Eigenvector Centralities. The results showed that the actor with the most connections (degree of centrality) and best communication control (betweenness centrality) is Parahyangan Catholic University (academic). At the same time, the actor that plays the most important role (eigenvector) is at the lower level of the government’s Sub-District and Urban Village. This study is useful for explaining the importance of the position of actors in the urban farming policy network, which is the key to the success of a program. Full article

Social entrepreneurship plays an important role in the maintenance of economic prosperity and brings benefits to society. The role of social entrepreneurship is growing in the light of challenges of the global economy, increasing uncertainty of the environment, the growth of social problems, and the emergence of crises in the 2020s. These derive an increase in economic and psychological challenges. Social entrepreneurship is known as the driver for solving global problems of society. The entrepreneurial ecosystem serves as a source of entrepreneurial opportunity, as a breeding ground for entrepreneurship. Therefore, exploring the topic of social entrepreneurship in the context of the entrepreneurial ecosystem becomes relevant. Social entrepreneurship, with respect to the entrepreneurial ecosystem, has been extensively explored. However, despite a growing body of publications, to the best of our knowledge, no bibliometric analysis is available on the topic. This analysis is important to understand what trends in the development of social entrepreneurship and the ecosystem exist, what further research directions can be recommended, and how the relationship between social entrepreneurship and the entrepreneurial ecosystem has been studied. This study aims to close the gap, consolidate research, and identify the state of the art in the field. In total, 357 publications from the Scopus database were selected for the period of 2009–2022. The study used social network analysis (bibliographic coupling network, co-citation network, citation network, and co-authorship network) and semantic analysis (semantic network) through VOSviewer version 1.6.19 and Gephi version 0.10.1 software. The results showed a growth of publications during this period, allowing us to observe influential journals, the most productive and cited authors, leading countries and universities, impactful papers, networks of collaborations, and co-citations of scholars. The paper with the highest degree of centrality is “Ecosystems in Support of Social Entrepreneurs: A Literature Review” while Sustainability is the most influential journal in the field. The analysis identified six thematic clusters within the research topic. The study contributes to the literature by presenting the research agenda, structure, characteristics of social entrepreneurship, and entrepreneurial ecosystem research. Full article

International trade relations have been considerably affected by the coronavirus pandemic. Our analysis was aimed at identifying its effect on the global trade network of green coffee beans, comparing the COVID-year 2020 to the pre-COVID year 2018. The methodology applied was that of social network analysis using trade value data for the above two years. Our results show that between the pre-pandemic and the pandemic years, the role of some major actors considerably changed, and many trade relationships were disrupted. Overall trade value decreased, and the number of trade connections also changed—some countries gained, but more countries lost compared to their former positions. The network measures, i.e., degree distribution, betweenness, closeness and eigenvector centralities, modularity-based clustering and the minimum spanning tree, were suitable for quantifying these changes and identifying differences between affected countries. The changes found between the two years are assumed to be due to the effects of the pandemic, but further analysis is needed to reveal the actual mechanisms leading to these results. Full article

The fundamental role of social networks in all areas of our lives and of social and political interactions is also very important in this new digital environment. The study focused on the analysis of tweets related to social-service issues published on Twitter during the different electoral campaigns in Spain from 2015 to 2019. The sample is 6728 tweets generated between 2015 and 2019 on the topic “social services” for quantitative analysis. In this analysis, we use the Gephi tool to observe how these messages flow on Twitter. The aim was to understand the socio-political discourse of different actors on social services in order to identify priority topics and networks for active Twitter profiles. The results show that users use Twitter for informal communication during the election period, focusing on messages, condemnation and positive evaluation to increase their visibility and influence. Full article

This paper attempts to examine the genealogical framework of “lamp records” (denglu 燈錄) of the Chan Buddhist tradition using analytical tools and methods of Historical Social Network Analysis (HSNA) and graph theory. As an exploratory study, the primary objectives are to investigate the possibilities offered by HSNA and visualization tools for research on Chan genealogy in lamp records, explore the benefits of this approach over traditional lineage charts, and reflect on its limitations. The essay focuses on the Chan community portrayed in the Goryeo 高麗 edition of the Zutang ji 祖堂集 (Collection of the Patriarchal Hall; K.1503). It shows that the lineage reportedly stemming from Qingyuan Xingsi 青原行思 (d. ca. 740) and Shitou Xiqian 石頭希遷 (701–791), as well as the branch descending from Tianhuang Daowu 天皇道悟 (748–807) to Xuefeng Yicun 雪峰義存 (822–908) and his successors, play a crucial role within the structure of the Zutang ji’s genealogical network. The study further highlights possible irregularities in lineage claims by contrasting metrics of degree and betweenness centrality with features of the text (e.g., number of hagiographic entries, length of the entries). Full article

The knowledge and innovation generated by researchers at universities is transferred to industries through patent licensing, leading to the commercialization of academic output. In order to investigate the development of Chinese university–industry technology transfer and whether this kind of collaboration may affect a firm’s innovation output, we collected approximately 6400 license contracts made between more than 4000 Chinese firms and 300 Chinese universities for the period between 2009 and 2014. This is the first study on Chinese university–industry knowledge transfer using a bipartite social network analysis (SNA) method, which emphasizes centrality estimates. We are able to investigate empirically how patent license transfer behavior may affect each firm’s innovative output by allocating a centrality score to each firm in the university–firm technology transfer network. We elucidate the academic–industry knowledge by visualizing flow patterns for different regions with the SNA tool, Gephi. We find that innovation capabilities, R&D resources, and technology transfer performance all vary across China, and that patent licensing networks present clear small-world phenomena. We also highlight the Bipartite Graph Reinforcement Model (BGRM) and BiRank centrality in the bipartite network. Our empirical results reveal that firms with high BGRM and BiRank centrality scores, long history, and fewer employees have greater innovative output. Full article

Today, students at universities in advanced countries typically enroll in colleges, such as the College of Education, which offer interdisciplinary programs for undergraduates in their first and second years, allowing them to explore personal interests, experience educational research fields, complete their integrated curricula, and then choose a major in their third year. To cooperate with the government’s epidemic prevention policies and measures in the post-COVID-19 era, the trend of e-learning and distance teaching has accelerated the establishment of integrated online curricula with interdisciplinary programs for undergraduates in the College of Education to facilitate effective future teacher professional development (TPD). Therefore, it is very important to construct e-learning curricula evaluation metrics for competency-based teacher professional development (CB-TPD) and to implement them in teaching practice. This research used social network analysis (SNA) methods, approaches, and theoretical concepts, such as affiliation networks and bipartite graphs comprised of educational occupational titles and common professional competencies (i.e., Element Name and ID), as well as knowledge, skills, abilities, and other characteristics (KSAOs), from the U.S. occupational information network (O*NET) 26.1 OnLine database, to collect data on the occupations of educational professionals. This study also used Gephi network analysis and visualization software to carry out descriptive statistics of keyword co-occurrences to measure their centrality metrics, including weighted degree centrality, degree centrality, betweenness centrality, and closeness centrality, and to verify their importance and ranking in professional competency in eight categories of educational professionals (i.e., three categories of special education teachers and five categories of teachers, except special education). The analysis of the centrality metrics identified the educational common professional competency (ECPC) keyword co-occurrences, which were then used to design, develop, and apply e-learning curricula evaluation metrics for CB-TPD. The results of this study can be used as a reference for conducting related academic research and cultivating educational professionals’ online curricula, including ECPC keywords, integrated curricula design and the development of transdisciplinary programs, and teacher education, as well as to facilitate the construction and application of future e-learning curricula evaluation metrics for CB-TPD. Full article

An important indicator for measuring the resilience and ability of urban networks to recover under external environmental shock, which is essential for the healthy development of the region, is urban network structure resilience. Herein we analyzed the resilience of the urban network structure and explored the influencing factors of resilience in the three provinces of Northeast China. We accomplished this by utilizing the Gephi profiling social network analysis tools based on the Baidu Index, road mileage, statistical data, other multi-source data, construction information, and the transportation, innovation, and economic multiple linkage network. This analysis enabled us to propose relevant suggestions and strategies to optimize urban network structure resilience. Our results indicate that (1) in 2019, the multi-city network structure in the three provinces of Northeast China contains both commonalities and characteristics. Overall, each network demonstrates a spatial distribution pattern of “dense in the north and sparse in the south.” (2) There exist evident hierarchical differences in the resilience characteristics of the multi-city network structure in the three provinces; each provincial capital city and sub-provincial city possesses greater advantages, the innovation network exhibits the most evident hierarchy, the mismatch of the information network is the highest, and the transmission and agglomeration of the economic network are the most prominent. (3) The resilience of the urban network structure of the three provinces is the result of the interaction of several factors. Political and economic factors such as government capacity, economic status, and urban vitality are the main factors affecting the resilience of the network structure. Full article

Large networks are quintessential to bioinformatics, knowledge graphs, social network analysis, and graph-based learning. CompositeView is a Python-based open-source application that improves interactive complex network visualization and extraction of actionable insight. CompositeView utilizes specifically formatted input data to calculate composite scores and display them using the Cytoscape component of Dash. Composite scores are defined representations of smaller sets of conceptually similar data that, when combined, generate a single score to reduce information overload. Visualized interactive results are user-refined via filtering elements such as node value and edge weight sliders and graph manipulation options (e.g., node color and layout spread). The primary difference between CompositeView and other network visualization tools is its ability to auto-calculate and auto-update composite scores as the user interactively filters or aggregates data. CompositeView was developed to visualize network relevance rankings, but it performs well with non-network data. Three disparate CompositeView use cases are shown: relevance rankings from SemNet 2.0, an open-source knowledge graph relationship ranking software for biomedical literature-based discovery; Human Development Index (HDI) data; and the Framingham cardiovascular study. CompositeView was stress tested to construct reference benchmarks that define breadth and size of data effectively visualized. Finally, CompositeView is compared to Excel, Tableau, Cytoscape, neo4j, NodeXL, and Gephi. Full article

Poland’s efforts to combat COVID-19 were hindered by endemic vaccination hesitancy and the prevalence of opponents to pandemic restrictions. In this environment, the policy of a COVID-19 vaccination mandate faces strong resistance in the public debate. Exploring the discourse around this resistance could help uncover the motives and develop an understanding of vaccination hesitancy in Poland. This paper aims to conduct a social network analysis and content analysis of Twitter discussions around the intention of the Polish Ministry of Health to introduce mandatory vaccinations for COVID-19. Twitter was chosen as a platform to study because of the critical role it played during the global health crisis. Twitter data were retrieved from 26 July to 9 December 2021 through the API v2 for Academic Research, and analysed using NodeXL and Gephi. When conducting social network analysis, nodes were ranked by their betweenness centrality. Clustering analysis with the Clauset–Newman–Moore algorithm revealed two important groups of users: advocates and opponents of mandatory vaccination. The temporal trends of tweets, the most used hashtags, the sentiment expressed in the most popular tweets, and correlations with epidemiological data were also studied. The results reveal a substantial degree of polarisation, a high intensity of the discussion, and a high degree of involvement of Twitter users. Vaccination mandate advocates were consistently more numerous, but less engaged and less mobilised to “preach” their own stances. Vaccination mandate opponents were vocal and more mobilised to participate: either as original authors or as information diffusers. Our research leads to the conclusion that systematic monitoring of the public debate on vaccines is essential not only in counteracting misinformation, but also in crafting evidence-based as well as emotionally motivating narratives. Full article

This paper is interdisciplinary and combines the research perspective of medical studies with that of media and social communication studies and theological studies. The main goal of this article is to determine [from arguments on all sides of the issue] whether, and to what extent, statements issued by a religious authority can be used as an argument in the COVID-19 vaccination campaign. The authors also want to find answers to the questions of how the pope’s comments affect public opinion when they concern the sphere of secular and everyday life, including issues related to health care. The main method used in this study is desktop research and the analysis of the Roman Catholic Church’s teaching on vaccination and on the types and significance of the pope’s statements on various topics. The auxiliary methods are sentiment analysis and network analysis made in the open source software Gephi. The authors are strongly interested in the communication and media aspect of the analyzed situation. Pope Francis’ voice on the COVID-19 vaccination has certainly been noticed and registered worldwide, but the effectiveness of his message and direct impact on Catholics’ decisions to accept or refuse the COVID-19 vaccination is quite questionable and would require further precise research. Comparing this to the regularities known from political marketing, one would think that the pope’s statement would not convince the firm opponents of vaccination. Full article

Resilience is an important issue in urban development, and community resilience (CR) is the most typical representative in building urban resilience, which has become the forefront of international resilience research. This paper presents a bibliometric and visual analysis of community resilience research collected from the WoS Core Collection database over the past two decades. H-index, citation frequency, centrality and starting year were adopted to analyze the research objects by bibliometric tools including CiteSpace, VOSviewer, and Gephi. The national and institutional characteristics of macro-geographical distribution and the characteristics of disciplines, journals, authors, and author cooperation of micro-knowledge network distribution were revealed. Finally, the potential research directions of community resilience in the future were discussed. The results show that there are three stages in community resilience research. Seven intellectual bases constitute the research background for community resilience, including social capital mechanism, the evolution of resilience knowledge, earthquake resistance and disaster mitigation, substance abuse, resilient development in rural communities, resilience-building in the least-developed countries, and emergency preparedness. Our analysis shows that the hottest community resilience research topics are the concept of resilience, climate resilience, the social capital mechanism, macro-environment and disaster-reduction policies, and an evaluation index system for community resilience. Full article