scholarly journals El componente social de la amenaza híbrida y su detección con modelos bayesianos/ The Social Component of the Hybrid Threat and its Detection with Bayesian Models

2019 ◽  
pp. 57-69 ◽  
Author(s):  
Ana-María Ruiz-Ruano ◽  
Jorge López-Puga ◽  
Juan-Jose Delgado-Morán
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Bayesian Networks ◽  
Computer Technology ◽  
Potential Utility ◽  
Redes Sociales ◽  
Social Component ◽  
The Social ◽  
Global Nature ◽  
Hybrid Threats

Las sociedades contemporáneas están cada vez más condicionadas por el desarrollo de la tecnología informática. Esa tendencia deja entrever un panorama en el que cada ser humano se identifica por el binomio persona-computadora, mientras que la mayor informatización de la vida civil está generando ingentes cantidades de datos que son susceptibles de ser gestionados con fines bélicos. El objetivo de este artículo es abordar la utilidad potencial de las redes bayesianas como herramientas destinadas a la monitorización y detección temprana de ataques híbridos de carácter social a escala global. Como conclusión, planteamos que el uso de la inferencia y las redes bayesianas es útil para monitorear, detectar y supervisar el componente social de las amenazas híbridas a escala global por medio del análisis de las redes sociales. Abstract Contemporary societies are increasingly conditioned by the development of computer technology. This trend suggests a picture in which each human being is identified by the person-computer binomial while greater computerization of civil life is generating huge amounts of data that are likely to be managed for war purposes. The objective of this article is to address the potential utility of Bayesian networks aimed at monitoring and early detection of hybrid attacks of a global nature. We conclude that the use of inference and Bayesian networks is useful for monitoring, detection and supervision of the social component of hybrid threats globally through social network analysis.

scholarly journals El análisis de redes sociales como instrumento del Trabajo Social frente a la COVID 19

2021 ◽  
pp. 35
Author(s):  
Joaquín Castillo de Mesa
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Interactions ◽  
Redes Sociales ◽  
Infected People ◽  
Social Nature ◽  
The Social ◽  
Community Transmission ◽  
Trabajo Social

El consenso científico señala que la mejor manera de frenar la propagación de la COVID-19 es mediante el rastreo de los contactos de las personas infectadas. Esta medida tiene carácter social, ya que busca analizar las redes de las personas para detectar de forma temprana quiénes están en riesgo de haber sido infectados, alertarles e imponerles la cuarentena, una medida de aislamiento social que impida la potencial propagación.  Hasta el momento mucho se ha hablado sobre qué profesionales deben realizar este rastreo pero poco acerca de cómo se debe realizar este rastreo. En este artículo, en primer lugar, se define qué es el rastreo, qué profesionales están más preparados para estas tareas y cómo se está llevando a cabo en España, encontrando ciertas carencias en cuanto al uso de metodologías científicas que apoyen la labor de rastreo. A partir de la literatura científica que analiza cómo afecta la socialización a la propagación se desarrolla una simulación sobre cómo se puede propagar la COVID 19 durante las interacciones sociales de las personas en sus distintos ámbitos de socialización. Sobre esta simulación se utiliza análisis de redes sociales y determinados algoritmos de detección de comunidades y de análisis de cohesión, para mostrar la idoneidad de estas metodologías para que el rastreo. Los resultados muestran que con el apoyo del análisis de redes sociales y de determinados algoritmos se accede de forma precoz a información clave sobre comunidades formadas en la estructura de red y sobre quiénes son los superpropagadores y los intermediadores entre las comunidades detectadas. Esto puede ayudar a priorizar la puesta en contacto con estas personas para cortar las cadenas de trasmisión comunitaria. Finalmente discutimos acerca de la idoneidad de que los profesionales del Trabajo Social se capaciten en estas metodologías para poder desarrollar esta labor del rastreo.Scientific consensus indicates that the best way to slow the spread of COVID 19 is by tracing the contacts of infected people. This measure has a social nature, since it seeks to analyze people’s networks to detect early who is at risk of being infected, alert them and impose quarantine, a measure of social isolation that prevents the potential spread. So far, much has been said about which professionals should perform this screening but Little about how it should be done. In this article, in the first place, it is defined what tracking is, which professionals are best prepared for the use of scientific methodologies that support tracking word. From the scientific literature that analyzes how socialization affects the spread, a simulation is developed on how COVID 19 can spread during the social interactions of people in their different areas of socialization. On this simulation, social network analysis and certain algorithms for community detection and cohesion analysis are used to show the suitability of these methodologies for tracking. The results show that with the support of social network analysis and certain algorithms, key information about communities formed in the network structure and who are the super-propagators and intermediaries between the detected communities is accessed early. This can help prioritize contacting these people to cut the chains of community transmission. Finally, we discuss the suitability for Social Work professionals to be trained in these methodologies in order to develop this tracking work.

scholarly journals Herramientas para el análisis y monitoreo en Redes Sociales

2011 ◽  
Vol 16 ◽  
pp. 33-40
Author(s):  
Juan José Prieto Gutiérrez
Keyword(s):  
Decision Making ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Redes Sociales ◽  
The Social ◽  
Decision Making Processes ◽  
Internet Networks ◽  
The Relationship ◽  
Network Of Networks

Networks or partnerships are used by humans since the beginning of humanity and its analysis raises concerns from many different sectors of society. In the era of the network of networks, Internet, networks are generated by virtual connections of the agents. Social Network Analysis (SNA) studies the relationship relation to each other, the social structure. It is an area that is emerging as essential in decision-making processes for its ability to analyze and intervene in the behaviour of structures. We analyze three NSA tools that monitor conversations on the Organization "IFLA" keyword in order to measure the feeling of them, managing social efforts to relate the flows between the entities, groups, etc.

Duality beyondPersons and Groups

2020 ◽  
pp. 391-413
Author(s):  
Sophie Mützel ◽  
Ronald Breiger
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Network Data ◽  
Future Directions ◽  
Affiliation Networks ◽  
The Social ◽  
Recent Developments ◽  
Principle Of Duality ◽  
Seminal Article

This chapter focuses on the general principle of duality, which was originally introduced by Simmel as the intersection of social circles. In a seminal article, Breiger formalized Simmel’s idea, showing how two-mode types of network data can be transformed into one-mode networks. This formal translation proved to be fundamental for social network analysis, which no longer needed data on who interacted with whom but could work with other types of data. In turn, it also proved fundamental for the analysis of how the social is structured in general, as many relations are dual (e.g. persons and groups, authors and articles, organizations and practices), and are thus susceptible to an analysis according to duality principles. The chapter locates the concept of duality within past and present sociology. It also discusses the use of duality in the analysis of culture as well as in affiliation networks. It closes with recent developments and future directions.

The Oxford Handbook of Social Networks

2020 ◽  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Life ◽  
Information Science ◽  
Computational Social Science ◽  
The Social ◽  
Diverse Field ◽  
The Rich

Social networks fundamentally shape our lives. Networks channel the ways that information, emotions, and diseases flow through populations. Networks reflect differences in power and status in settings ranging from small peer groups to international relations across the globe. Network tools even provide insights into the ways that concepts, ideas and other socially generated contents shape culture and meaning. As such, the rich and diverse field of social network analysis has emerged as a central tool across the social sciences. This Handbook provides an overview of the theory, methods, and substantive contributions of this field. The thirty-three chapters move through the basics of social network analysis aimed at those seeking an introduction to advanced and novel approaches to modeling social networks statistically. The Handbook includes chapters on data collection and visualization, theoretical innovations, links between networks and computational social science, and how social network analysis has contributed substantively across numerous fields. As networks are everywhere in social life, the field is inherently interdisciplinary and this Handbook includes contributions from leading scholars in sociology, archaeology, economics, statistics, and information science among others.

scholarly journals Spanish communication academia: Scientific productivity vs. Social activity

Comunicar ◽  
2013 ◽  
Vol 21 (41) ◽  
pp. 61-70 ◽  
Author(s):  
Cristóbal Casanueva-Rocha ◽  
Francisco-Javier Caro-González
Keyword(s):  
Social Network ◽  
Social Factors ◽  
Social Activity ◽  
Scientific Productivity ◽  
Scientific Activity ◽  
Redes Sociales ◽  
International Scope ◽  
The Social ◽  
La Red ◽  
The Impact

At a time when academic activity in the area of communication is principally assessed by the impact of scientific journals, the scientific media and the scientific productivity of researchers, the question arises as to whether social factors condition scientific activity as much as these objective elements. This investigation analyzes the influence of scientific productivity and social activity in the area of communication. We identify a social network of researchers from a compilation of doctoral theses in communication and calculate the scientific production of 180 of the most active researchers who sit on doctoral committees. Social network analysis is then used to study the relations that are formed on these doctoral thesis committees. The results suggest that social factors, rather than individual scientific productivity, positively influence such a key academic and scientific activity as the award of doctoral degrees. Our conclusions point to a disconnection between scientific productivity and the international scope of researchers and their role in the social network. Nevertheless, the consequences of this situation are tempered by the nonhierarchical structure of relations between communication scientists. En un momento en que la actividad académica en el ámbito de la comunicación se valora principalmente por el impacto de las revistas y los medios de comunicación científica y por la productividad de los investigadores, surge la cuestión de si los factores sociales pueden condicionar la actividad científica con la misma fuerza que estos elementos objetivos. Esta investigación analiza la influencia de la productividad científica y de la actividad social en el ámbito de la comunicación. Se ha identificado la red social de los investigadores de comunicación a partir de las tesis doctorales. Para los 180 investigadores más activos en los tribunales de tesis se ha calculado su producción científica. Se utiliza el análisis de redes sociales para estudiar las relaciones que se producen en los tribunales de tesis doctorales. Los resultados muestran que los factores sociales influyen positivamente en una actividad académica y científica tan relevante como la obtención del grado de doctor, mientras que la productividad científica individual no lo hace. Como conclusiones cabe señalar que existe una desconexión entre la productividad científica y la proyección internacional de los investigadores y su papel en la red social. Las implicaciones de este hecho están matizadas por una estructura no jerarquizada de las relaciones entre los científicos de comunicación.

scholarly journals Evaluation of Coordination of Emergency Response Team through the Social Network Analysis. Case Study: Oil and Gas Refinery

2015 ◽  
Vol 6 (1) ◽  
pp. 30-34 ◽  
Author(s):  
Iraj Mohammadfam ◽  
Susan Bastani ◽  
Mahbobeh Esaghi ◽  
Rostam Golmohamadi ◽  
Ali Saee
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Emergency Response ◽  
Oil And Gas ◽  
The Social ◽  
Response Team ◽  
Gas Refinery

scholarly journals The use of social network methodological framework in nursing care to breastfeeding women

2009 ◽  
Vol 17 (3) ◽  
pp. 354-360 ◽  
Author(s):  
Maria Helena do Nascimento Souza ◽  
Ivis Emília de Oliveira Souza ◽  
Florence Romijn Tocantins
Keyword(s):  
Social Network ◽  
Social Context ◽  
Network Analysis ◽  
Nursing Care ◽  
Nursing Practice ◽  
Methodological Framework ◽  
Strong Bond ◽  
The People ◽  
Primary Network ◽  
The Social

This study aimed to discuss the contribution of the social network methodological framework in nursing care delivered to women who breastfeed their children up to six months of age. This qualitative study aimed to elaborate the social network map of 20 women through tape-recorded interview. Social network analysis evidenced a "strong" bond between these women and members from their primary network, especially friends, neighbors, mothers or with the child's father, who were reported as the people most involved in the breastfeeding period. The contribution of this framework to nursing practice is discussed, especially in care and research processes. We believe that nurses' appropriation of this framework can be an important support for efficacious actions, as well as to favor a broader perspective on the social context people experience.

The evolution of the intellectual structure of strategic management between 1980 and 2019

2020 ◽  
Vol 13 (4) ◽  
pp. 503-534
Author(s):  
Mehmet Ali Köseoğlu ◽  
John Parnell
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Strategic Management ◽  
Citation Analysis ◽  
Intellectual Structure ◽  
Future Directions ◽  
Content Type ◽  
The Social ◽  
Economic Foundation

PurposeThe authors evaluate the evolution of the intellectual structure of strategic management (SM) by employing a document co-citation analysis through a network analysis for academic citations in articles published in the Strategic Management Journal (SMJ).Design/methodology/approachThe authors employed the co-citation analysis through the social network analysis.FindingsThe authors outlined the evolution of the academic foundations of the structure and emphasized several domains. The economic foundation of SM research with macro and micro perspectives has generated a solid knowledge stock in the literature. Industrial organization (IO) psychology has also been another dominant foundation. Its robust development and extension in the literature have focused on cognitive issues in actors' behaviors as a behavioral foundation of SM. Methodological issues in SM research have become dominant between 2004 and 2011, but their influence has been inconsistent. The authors concluded by recommending future directions to increase maturity in the SM research domain.Originality/valueThis is the first paper to elucidate the intellectual structure of SM by adopting the co-citation analysis through the social network analysis.

scholarly journals Analisis Akun Twitter Berpengaruh terkait Covid-19 menggunakan Social Network Analysis

2021 ◽  
Vol 5 (4) ◽  
pp. 697-704
Author(s):  
Aprillian Kartino ◽  
M. Khairul Anam ◽  
Rahmaddeni ◽  
Junadhi
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Information Dissemination ◽  
Interaction Network ◽  
Analysis Method ◽  
The Social ◽  
The World ◽  
High Degree

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.  

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