How Members of Covert Networks Conceal the Identities of Their Leaders

Centrality measures are the most commonly advocated social network analysis tools for identifying leaders of covert organizations. While the literature has predominantly focused on studying the effectiveness of existing centrality measures or developing new ones, we study the problem from the opposite perspective, by focusing on how a group of leaders can avoid being identified by centrality measures as key members of a covert network. More specifically, we analyze the problem of choosing a set of edges to be added to a network to decrease the leaders’ ranking according to three fundamental centrality measures, namely, degree, closeness, and betweenness. We prove that this problem is NP-complete for each measure. Moreover, we study how the leaders can construct a network from scratch, designed specifically to keep them hidden from centrality measures. We identify a network structure that not only guarantees to hide the leaders to a certain extent but also allows them to spread their influence across the network.

The Rise of K-Popper's Political Awareness in Twitter and What It Means for All of Us

The Omnibus Law, which was passed on October 5, 2020, has discontented students and workers who protested on the streets. The ratification of the Omnibus Law has an effect on public opinion-raising activities, both those who support and who reject the ratification of the Omnibus Law, are crowded on Twitter social media. The active account of K-Poppers who took part became a line against the opinion of the Omnibus Law, so a question arises whether this is a phenomenon of the rise of political awareness of K-Poppers in Indonesia? This study analyzes the role of K-Poppers in socio-political movements in several countries globally, especially in Indonesia. The type of research used is a case study of the K-Poppers movement in the case of the ratification of the Omnibus Law as the subject in writing this paper. Collecting data using Social Network Analysis (SNA) and observing the activities of K-Poppers in Indonesia when parliament passed the controversial Omnibus Law. The results of this study indicate that Indonesian K-Poppers maximize their function as part of Indonesian citizens to express their political stance. They also showed their political involvement when creating hashtags, organizing other K-Pop crowds, and at the same time providing support to activists who rejected the Omnibus Law.

The use of social network analysis in social support and care: a systematic scoping review protocol

Background In recent decades, the literature on Social Network Analysis and health has experienced a significant increase. Disease transmission, health behavior, organizational networks, social capital, and social support are among the different health areas where Social Network Analysis has been applied. The current epidemiological trend is characterized by a progressive increase in the population’s ageing and the incidence of long-term conditions. Thus, it seems relevant to highlight the importance of social support and care systems to guarantee the coverage of health and social needs within the context of acute illness, chronic disease, and disability for patients and their carers. Thus, the main aim is to identify, categorize, summarize, synthesize, and map existing knowledge, literature, and evidence about the use of Social Network Analysis to study social support and care in the context of illness and disability. Methods This scoping review will be conducted following Arksey and O'Malley's framework with adaptations from Levac et al. and Joanna Briggs Institute’s methodological guidance for conducting scoping reviews. We will search the following databases (from January 2000 onwards): PubMed, MEDLINE, Web of Science Core Collection, SCOPUS, CINAHL, PsycINFO, Cochrane Database of Systematic Reviews, PROSPERO, and DARE. Complementary searches will be conducted in selected relevant journals. Only articles related to social support or care in patients or caregivers in the context of acute illnesses, disabilities or long-term conditions will be considered eligible for inclusion. Two reviewers will screen all the citations, full-text articles, and abstract the data independently. A narrative synthesis will be provided with information presented in the main text and tables. Discussion The knowledge about the scientific evidence available in the literature, the methodological characteristics of the studies identified based on Social Network Analysis, and its main contributions will highlight the importance of health-related research's social and relational dimensions. These results will shed light on the importance of the structure and composition of social networks to provide social support and care and their impact on other health outcomes. It is anticipated that results may guide future research on network-based interventions that might be considered drivers to provide further knowledge in social support and care from a relational approach at the individual and community levels. Trial registration Open Science Framework https://osf.io/dqkb5.

Initial social network analysis of producers working towards sustainability suggests weak ties and potential fragmentation

For farmers to adopt and maintain sustainable farming practices, they must have the resources and network to succeed with this work and must realize a positive impact on their business model. As a food system is ultimately made up of the people, organizations, and institutions that grow, move, buy and sell food, we must understand who is at the center of this network, who is well-connected, and who is peripheral. Within a particular regional food system in a highly productive southeastern U.S. state, the network of local producers interested in sustainable production, including environmental and economic components, seems to be growing. However, it is unclear who benefits from this system and whether this system is growing in a way that encourages and enhances the benefits for sustainable agriculture. Existing evidence for the network size and its vulnerabilities has been anecdotal, from Extension agents and their contacts with individual producers, rather than based on systematic research. We used social network analysis to understand the status of the system and its constituents. Connections between producers appear to be weak overall with potential fragmentation, suggesting a fragility that could easily derail efforts to increase sustainable production in the region.

BIM Engineering Cost Budgeting Method based on Social Network Analysis Method

Engineering cost budgeting is crucial to the healthy development of the engineering and construction industry. To solve the problem of large deviations in budget results from using traditional methods of cost budgeting based on evaluation results or drawing conversions, the BIM engineering cost budgeting method based on social network analysis method is studied in this paper. On the basis of analyzing cost budgeting requirements and determining modeling economic criteria, the social network analysis method is used to analyze the project cost risks. According to the project quantity list, professional BIM software is used to budget the project cost. Case verification proves the feasibility of this budgeting method as the budget deviation is reduced by about 78% compared with the traditional method. The result of the paper has a more ideal practical application effect and positive significance for optimizing the traditional cost budgeting methods

Big Data Analysis of the Key Attributes Related to Stress and Mental Health in Korean Taekwondo Student Athletes

In the present study, we used big data analysis to examine the key attributes related to stress and mental health among Korean Taekwondo student-athletes. Keywords included “Taekwondo + Student athlete + Stress + Mental health”. Naver and Google databases were searched to identify research published between 1 January 2010 and 31 December 2019. Text-mining analysis was performed on unstructured texts using TEXTOM 4.5, with social network analysis performed using UCINET 6. In total, 3149 large databases (1.346 MB) were analyzed. Two types of text-mining analyses were performed, namely, frequency analysis and term frequency-inverse document frequency analysis. For the social network analysis, the degree centrality and convergence of iterated correlation analysis were used to deduce the node-linking degree in the network and to identify clusters. The top 10 most frequently used terms were “stress”, “Taekwondo”, “health”, “player”, “student”, “mental”, “exercise”, “mental health”, “relieve”, and “child.” The top 10 most frequently occurring results of the TF-IDF analysis were “Taekwondo”, “health”, “player”, “exercise”, “student”, “mental”, “stress”, “mental health”, “child” and “relieve”. The degree centrality analysis yielded similar results regarding the top 10 terms. The convergence of iterated correlation analysis identified six clusters: student, start of dream, diet, physical and mental, sports activity, and adult Taekwondo center. Our results emphasize the importance of designing interventions that attenuate stress and improve mental health among Korean Taekwondo student-athletes.