scholarly journals Wars of the World: Evaluating the Global Conflict Structure During the Years 1816-2001 Using Social Network Analysis

2013 ◽  
Vol 100 ◽  
pp. 68-79 ◽  
Author(s):  
Olga Levina ◽  
Robert Hillmann
2021 ◽  
Vol 5 (4) ◽  
pp. 697-704
Author(s):  
Aprillian Kartino ◽  
M. Khairul Anam ◽  
Rahmaddeni ◽  
Junadhi

Covid-19 is a disease of the virus that is shaking the world and has been designated by WHO as a pandemic. This case of Covid-19 can be a place of dissemination of disinformation that can be utilized by some parties. The dissemination of information in this day and age has turned to the internet, namely social media, Twitter is one of the social media that is often used by Indonesians and the data can be analyzed. This study uses the social network analysis method, conducted to be able to find nodes that affect the ongoing interaction in the interaction network of information dissemination related to Covid-19 in Indonesia and see if the node is directly proportional to the value of its popularity. As well as to know in identifying the source of Covid-19 information, whether dominated by competent Twitter accounts in their fields. The data examined 19,939 nodes and 12,304 edges were taken from data provided by the web academic.droneemprit.id on the project "Analisis Opini Persebaran Virus Corona di Media Sosial", using the period of December 2019 to December 2020 on social media Twitter. The results showed that the @do_ra_dong account is an influential actor with the highest degree centrality of 860 and the @detikcom account is the actor with the highest popularity value of follower rank of 0.994741605. Thus actors who have a high degree of centrality value do not necessarily have a high follower rank value anyway. The study ignores if there are buzzer accounts on Twitter.  


Author(s):  
Lucas G. S. Felix ◽  
Carlos M. Barbosa ◽  
Vinícius da F. Vieira ◽  
Carolina Ribeiro Xavier

Soccer is the most popular sport in the world and due its popularity, soccer moves billions of euros over the years, in most diverse forms, such as marketing, merchandising, TV quotas and players transfers. As example, in the 2016/2017 season, only England has moved about 1.3 billion of euros only in players transfers. In this work, it is performed a study of the transfer market of player. To do so, players transfer data were gathered from the website Transfermarkt and were modeled as a graph. In order to perform this study, different Complex Networks techniques were applied, such as Overlap Community Detection and Property Analysis. Through our results we could evaluate the soccer players market, and see a pattern that every market has at least one farm country, which has a main function of selling athletes, or a buyer country, which most of its transactions is buying players.


2020 ◽  
Author(s):  
CHIEN WEI

UNSTRUCTURED The recent article published on December 1 in 2020 is well-written but remains several questions that are required to clarifications further, including (1) how many out of those 543 articles were published in Pubmed, (2) whether visualizations can be applied to the study, particularly, with citation analysis, and (3)the article lacks the method to category the overall sentiment about the usefulness of telehealth and analysis to quantify the research contributions in countries/regions to the world. We replicated a study using the similar collected articles in Pubmed to (1) visualize the research contributions in countries/regions and journals using citation metrics, and (2) demonstrate the semantic analysis applied to category article topics related to journals and citations. A total of 514 similar articles extracted from the previous study were collected to match the number of citations in Pubmed. The x-index was used to evaluate research contributions to the COVID-19 epidemic for countries/regions and journals shown on a choropleth map and Kano diagram, respectively. The semantic analysis was performed using abstracts to category article topics related to journals using social network analysis.


Author(s):  
Phu Ngoc Vo ◽  
Tran Vo Thi Ngoc

Many different areas of computer science have been developed for many years in the world. Data mining is one of the fields which many algorithms, methods, and models have been built and applied to many commercial applications and research successfully. Many social networks have been invested and developed in the strongest way for the recent years in the world because they have had many big benefits as follows: they have been used by lots of users in the world and they have been applied to many business fields successfully. Thus, a lot of different techniques for the social networks have been generated. Unsurprisingly, the social network analysis is crucial at the present time in the world. To support this process, in this book chapter we have presented many simple concepts about data mining and social networking. In addition, we have also displayed a novel model of the data mining for the social network analysis using a CLIQUE algorithm successfully.


2013 ◽  
Vol 821-822 ◽  
pp. 667-672
Author(s):  
Hao Zhou ◽  
Xiao Li Li

This paper takes 31 leading textile and apparel trade countries or regions in the world as the research object. It constructs the adjacency matrix for the trade relations. Using social network analysis method, it measures the network structure of the global textile and apparel trade respectively from the perspectives of trade network diagram, network density, centrality. According to this result, it explores Chinas position on the global textile and apparel trade and interprets it.


2019 ◽  
Vol 24 (6) ◽  
pp. 821-854 ◽  
Author(s):  
Sunil Babbar ◽  
Xenophon Koufteros ◽  
Ravi S. Behara ◽  
Christina W.Y. Wong

Purpose This study aims to examine publications of supply chain management (SCM) researchers from across the world and maps the leadership role of authors and institutions based on how prolific they are in publishing and on network measures of centrality while accounting for the quality of the outlets that they publish in. It aims to inform stakeholders on who the leading SCM scholars are, their primary areas of SCM research, their publication profiles and the nature of their networks. It also identifies and informs on the leading SCM research institutions of the world and where leadership in specific areas of SCM research is emerging from. Design/methodology/approach Based on SCM papers appearing in a set of seven leading journals over the 15-year period of 2001-2015, publication scores and social network analysis measures of total degree centrality and Bonacich power centrality are used to identify the highest ranked agents in SCM research overall, as well as in some specific areas of SCM research. Social network analysis is also used to examine the nature and scope of the networks of the ranked agents and where leadership in SCM research is emerging from. Findings Authors and institutions from the USA and UK are found to dominate much of the rankings in SCM research both by publication score and social network analysis measures of centrality. In examining the networks of the very top authors and institutions of the world, their networks are found to be more inward-looking (country-centric) than outward-looking (globally dispersed). Further, researchers in Europe and Asia alike are found to exhibit significant continental inclinations in their network formations with researchers in Europe displaying greater propensity to collaborate with their European-based counterparts and researchers in Asia with their Asian-based counterparts. Also, from among the journals, Supply Chain Management: An International Journal is found to exhibit a far more expansive global reach than any of the other journals. Research limitations/implications The journal set used in this study, though representative of high-quality SCM research outlets, is not exhaustive of all potential outlets that publish SCM research. Further, the measure of quality that this study assigns to the various publications is based solely on a publication score that accounts for the quality of the journals, as rated by Association of Business Schools that the papers appear in and nothing else. Practical implications By informing the community of stakeholders of SCM research about the top-ranked SCM authors, institutions and countries of the world, the nature of their networks, as well as what the primary areas of SCM research of the leading authors in the world are, this research provides stakeholders, including managers, researchers and students, information that is helpful to them not only because of the insights it provides but also for the gauging of potential for embedding themselves in specific networks, engaging in collaborative research with the leading agents or pursuing educational opportunities with them. Originality/value This research is the first of its kind to identify and rank the top SCM authors and institutions from across the world using a representative set of seven leading SCM and primary OM journals based on publication scores and social network measures of centrality. The research is also the first of its kind to identify and rank the top authors and institutions within specific areas of SCM research and to identify future research opportunities relating to aspects of collaboration and networking in research endeavors.


2021 ◽  
Vol 11 (5) ◽  
pp. 2253
Author(s):  
Katarina Kostelić ◽  
Marko Turk

The applications of social network analysis to the world tourism network are scarce, and a research update is long overdue. The goal of this research is to examine the topology of the world tourism network and to discuss the meaning of its characteristics in light of the current situation. The data used for the analysis comprise 193 target countries, 242 source countries, and 17,022 links, which is an overall 1,448,285,894 travels in 2018. Social network analysis is applied to the data to determine network topological and diffusion properties, as well as the network structure and its regularities (does it behave more as a social or a technological/biological network?). While results presented in this paper give a thorough insight into the world tourism network in the year 2018, they are only a glimpse in comparison to the possibilities for further research.


Author(s):  
Kyent-Yon Yie ◽  
Tsair-Wei Chien ◽  
Yu-Tsen Yeh ◽  
Willy Chou ◽  
Shih-Bin Su

The COVID-19 pandemic has spread widely around the world. Many mathematical models have been proposed to investigate the inflection point (IP) and the spread pattern of COVID-19. However, no researchers have applied social network analysis (SNA) to cluster their characteristics. We aimed to illustrate the use of SNA to identify the spread clusters of COVID-19. Cumulative numbers of infected cases (CNICs) in countries/regions were downloaded from GitHub. The CNIC patterns were extracted from SNA based on CNICs between countries/regions. The item response model (IRT) was applied to create a general predictive model for each country/region. The IP days were obtained from the IRT model. The location parameters in continents, China, and the United States were compared. The results showed that (1) three clusters (255, n = 51, 130, and 74 in patterns from Eastern Asia and Europe to America) were separated using SNA, (2) China had a shorter mean IP and smaller mean location parameter than other counterparts, and (3) an online dashboard was used to display the clusters along with IP days for each country/region. Spatiotemporal spread patterns can be clustered using SNA and correlation coefficients (CCs). A dashboard with spread clusters and IP days is recommended to epidemiologists and researchers and is not limited to the COVID-19 pandemic.


2021 ◽  
pp. 37-64
Author(s):  
Helen Seitzer ◽  
Fabian Besche-Truthe ◽  
Michael Windzio

AbstractIn Chap. 10.1007/978-3-030-78885-8_2, Helen Seitzer, Fabian Besche-Truthe, and Michael Windzio investigate the diffusion of compulsory education from a global perspective. Compulsory education closely relates to the reproduction and change of a country’s culture.  In this chapter, the authors focus on the effect of a country’s membership in different clusters defined by cultural characteristics, on the diffusion of compulsory education. They apply social network analysis to define global ‘cultural spheres’, which have fuzzy boundaries. This network is the structural framework behind the diffusion process of compulsory education. The impact of cultural spheres on diffusion is tested by exposure in terms of close ties to other countries with compulsory education, and they are found to significantly increase the rate of adoption.


Sign in / Sign up

Export Citation Format

Share Document