scholarly journals A Naming Game-Based Method for the Location of Information Source in Social Networks

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Xue Yang ◽  
Zhiliang Zhu ◽  
Hai Yu ◽  
Yuli Zhao
Keyword(s):  
Social Networks ◽  
Information Source ◽  
Information Propagation ◽  
Propagation Models ◽  
Naming Game ◽  
Dynamic Deployment ◽  
True Source ◽  
Novel Method ◽  
Simulation Results ◽  
Source Of Information

We study herein the problem of the location of the information propagation source in social networks based on the network topology and a set of observations. We propose a concise and novel method to accurately locate the source of information using naming game theory. This study introduces the design of a dynamic deployment method that reduces considerably the number of observations and the time needed to locate the source. Moreover, it calculates the probability of each node that acts as a source based on the information provided by observations. This method can be potentially applied to various information propagation models. The simulation results reveal that the method is able to estimate the information source within a small number of hops from the true source.

scholarly journals Dinámicas de información en profesionales de enfermería desde el análisis de redes sociales

2021 ◽  
Vol 20 (3) ◽  
pp. 55-85
Author(s):  
Maria-Lara Martínez-Gimeno ◽  
Maria-Antonia Ovalle-Perandones ◽  
Gema Escobar-Aguilar ◽  
Nélida Fernández-Martínez ◽  
Jose Alberto Benítez- Andrades ◽  
...  
Keyword(s):  
Social Networks ◽  
Information Source ◽  
Redes Sociales ◽  
Information Dynamics ◽  
Research Results ◽  
Hospital Units ◽  
Validated Questionnaire ◽  
Horizontal Networks ◽  
Nursing Professionals ◽  
Source Of Information

Introducción: El conocimiento es una herramienta necesaria para la investigación científica y el progreso de cualquier disciplina. Pero el conocimiento científico y las dinámicas de información no sólo están sostenidas por los individuos, sino que son producidas y mantenidas por grupos de personas que trabajan en un mismo entorno donde los vínculos y las relaciones pueden influir en el proceso. Objetivo: Analizar las redes sociales de utilización de fuentes de información, de ayuda/consejo para la transferencia de conocimiento y los lugares donde los profesionales de enfermería comparten información.Método: Análisis de Redes Sociales a través de un cuestionario validado. Se reclutaron profesionales de 6 unidades hospitalarias.Resultados: Participaron 77 profesionales con una edad media de 42,9 (DE:11,48). Los compañeros son la fuente de información más utilizada (76 elecciones) frente a las bases de datos y artículos científicos que son la menos seleccionada (63 elecciones). Las redes homófilas horizontales (profesionales con estatus/intereses similares) son las más frecuentes para obtener información sobre resultados de investigación (74 elecciones). La unidad asistencial es el entorno más señalado para compartir información (50 elecciones).Conclusiones: Los profesionales consideran el conocimiento de sus compañeros como la principal fuente para obtener información sobre resultados de investigación. Unidades con determinado grado de especialización utilizan guías de práctica clínica y protocolos como fuente principal de información. Los profesionales de enfermería utilizan redes homófilas-horizontales para obtener información. El entorno laboral en sus diferentes ámbitos (unidad, office, reuniones) es el más utilizado para compartir información sobre resultados de investigación. Introduction: Knowledge is a necessary tool for scientific research and progress in any discipline. But scientific knowledge and information dynamics are not only sustained by individuals but are produced and maintained by groups of people working in the same environment where links and relationships can influence the process. Aim: To analyze the social networks of information source utilization, help/advice for knowledge transfer and the places where nursing professionals share information.Method: Analysis of social networks through a validated questionnaire. Professionals from 6 hospital units were recruited.Results: 77 professionals participated with a mean age of 42.9 (SD:11.48). Peers were the most frequently used source of information (76 choices) compared to databases and scientific articles, which were the least selected (63 choices). Horizontal homophilous networks (professionals with similar status/interests) are the most frequent for obtaining information on research results (74 choices). The care unit is the most pointed environment for sharing information (50 choices).Conclusions: Professionals consider the knowledge of their peers as the main source for obtaining information on research results. Units with a certain degree of specialization use clinical practice guidelines and protocols as the main source of information. Nursing professionals use homophilic-horizontal networks to obtain information. The work environment in its different settings (unit, office, meetings) is the most used for sharing information on research results.

scholarly journals Peculiarities of using social networks as a source of information in online media

2020 ◽  
pp. 230-242
Author(s):  
Mariana Kitsa
Keyword(s):  
Social Networks ◽  
Content Analysis ◽  
Information Source ◽  
Media Content Analysis ◽  
Internet Media ◽  
Keywords Internet ◽  
The Social ◽  
The Media ◽  
Information Materials ◽  
Source Of Information

Social networks are a modern communication trend performing various functions in society. One of them is a source of information for the media. The article examines theoretical aspects of usage of social networks in Internet media, content analysis of Internet media materials that use information from social networks, highlights the topics of such materials, ways to use information from social networks, evidence and argumentation of information. In order to achieve this goal, we conducted a study that consisted of several stages. The first is an analysis of theoretical basis. We have studied the research of the Ukrainian and foreign researchers on social networks and the possibilities of using them as a source of information in journalism. After that, a content analysis of materials of all-Ukrainian and regional Internet media was carried out for the presence of links to social networks, in particular text links, screens, photos or videos. Technologically, it looked like this: we introduced a search query for the phrases «social networks», «in the social network», «form social networks» in search engines, filtering the results in the news for April-May 2020. It produced almost 200 results (N = 197). The object of content analysis was information materials placed in the newsfeed, as well as publications placed in the top sections. Next, we analyzed the topic, the argumentation of the materials, the availability of additional comments, links to official sources, resources, and so on. We also singled out those materials whose content seemed questionable, contained emotional headlines, insufficient evidence, manipulation, and so on. The conclusions state that despite the multi-vector use of information from social networks in the Internet media, journalists must adhere to journalistic standards, check the information so as not to spread misinformation. Keywords: Internet media, social networks, information, source.

A Strong Tracking Cubature Kalman Filter for Nonlinear Estimation

2013 ◽  
Vol 313-314 ◽  
pp. 1115-1119
Author(s):  
Yong Qi Wang ◽  
Feng Yang ◽  
Yan Liang ◽  
Quan Pan
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Scaling Factor ◽  
Estimation Accuracy ◽  
Nonlinear State Estimation ◽  
Cubature Kalman Filter ◽  
Novel Method ◽  
Simulation Results ◽  
Tracking Filter ◽  
Nonlinear State

In this paper, a novel method based on cubature Kalman filter (CKF) and strong tracking filter (STF) has been proposed for nonlinear state estimation problem. The proposed method is named as strong tracking cubature Kalman filter (STCKF). In the STCKF, a scaling factor derived from STF is added and it can be tuned online to adjust the filtering gain accordingly. Simulation results indicate STCKF outperforms over EKF and CKF in state estimation accuracy.

Influences of Stress on Interactions with Decision Support

2020 ◽  
Vol 64 (1) ◽  
pp. 1886-1890
Author(s):  
Kylie Litaker ◽  
Christopher B. Mayhorn
Keyword(s):  
Decision Support ◽  
Information Source ◽  
Negative Influence ◽  
Future Research ◽  
Physiological Measures ◽  
Task Motivation ◽  
Situational Stress ◽  
Before And After ◽  
Scanning Task ◽  
Source Of Information

People regularly interact with automation to make decisions. Research shows that reliance on recommendations can depend on user trust in the decision support system (DSS), the source of information (i.e. human or automation), and situational stress. This study explored how information source and stress affect trust and reliance on a DSS used in a baggage scanning task. A preliminary sample of sixty-one participants were given descriptions for a DSS and reported trust before and after interaction. The DSS gave explicit recommendations when activated and participants could choose to rely or reject the choice. Results revealed a bias towards self-reliance and a negative influence of stress on trust, particularly for participants receiving help from automation. Controlling for perceived reliability may have eliminated trust biases prior to interaction, while stress may have influenced trust during the task. Future research should address potential differences in task motivation and include physiological measures of stress.

scholarly journals The effect of social distancing on the reach of an epidemic in social networks

2021 ◽  
Author(s):  
Gregory Gutin ◽  
Tomohiro Hirano ◽  
Sung-Ha Hwang ◽  
Philip R. Neary ◽  
Alexis Akira Toda
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Social Networks ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Disease Transmission ◽  
Global Level ◽  
Social Distancing ◽  
The Public ◽  
Simulation Results

AbstractHow does social distancing affect the reach of an epidemic in social networks? We present Monte Carlo simulation results of a susceptible–infected–removed with social distancing model. The key feature of the model is that individuals are limited in the number of acquaintances that they can interact with, thereby constraining disease transmission to an infectious subnetwork of the original social network. While increased social distancing typically reduces the spread of an infectious disease, the magnitude varies greatly depending on the topology of the network, indicating the need for policies that are network dependent. Our results also reveal the importance of coordinating policies at the ‘global’ level. In particular, the public health benefits from social distancing to a group (e.g. a country) may be completely undone if that group maintains connections with outside groups that are not following suit.

scholarly journals Taking sociality seriously: the structure of multi-dimensional social networks as a source of information for individuals

2012 ◽  
Vol 367 (1599) ◽  
pp. 2108-2118 ◽  
Author(s):  
Louise Barrett ◽  
S. Peter Henzi ◽  
David Lusseau
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Life ◽  
The Other ◽  
Feedback Process ◽  
Cognitive Evolution ◽  
Systems Approaches ◽  
Entropy Measures ◽  
Free Ranging ◽  
Source Of Information

Understanding human cognitive evolution, and that of the other primates, means taking sociality very seriously. For humans, this requires the recognition of the sociocultural and historical means by which human minds and selves are constructed, and how this gives rise to the reflexivity and ability to respond to novelty that characterize our species. For other, non-linguistic, primates we can answer some interesting questions by viewing social life as a feedback process, drawing on cybernetics and systems approaches and using social network neo-theory to test these ideas. Specifically, we show how social networks can be formalized as multi-dimensional objects, and use entropy measures to assess how networks respond to perturbation. We use simulations and natural ‘knock-outs’ in a free-ranging baboon troop to demonstrate that changes in interactions after social perturbations lead to a more certain social network, in which the outcomes of interactions are easier for members to predict. This new formalization of social networks provides a framework within which to predict network dynamics and evolution, helps us highlight how human and non-human social networks differ and has implications for theories of cognitive evolution.

A Novel Method of Detecting Measurement Uncertainty of Voltage Deviation

2013 ◽  
Vol 805-806 ◽  
pp. 688-692
Author(s):  
Xin Fang ◽  
Xue Liang Huang ◽  
Yan Zhu
Keyword(s):  
Measurement Uncertainty ◽  
Power Quality ◽  
National Standards ◽  
Measurement Time ◽  
Time Interval ◽  
Basic Measurement ◽  
Voltage Deviation ◽  
Novel Method ◽  
Simulation Results

Nowadays, there are various devices to detect the power quality of AC grid, where uncertainty of voltage deviation is an important parameter to investigate the power quality. National standards specify several sinusoidal waveforms to detect it, usually implemented into the detecting devices. But these waveforms are not enough and a novel method of detecting measurement uncertainty of voltage deviation is proposed in this paper. A series of detection waveforms are designed using this method. The simulation results verify that the method is available to measure uncertainty of voltage deviation more accurately. Moreover, it can be used to justify whether the basic measurement time interval of voltage deviation meets IEC standard requirements.

Community detection in social networks based on information propagation and user engagement

2021 ◽  
pp. 2150119
Author(s):  
Fuzhong Nian ◽  
Li Luo ◽  
Xuelong Yu ◽  
Xin Guo
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Detection Method ◽  
User Engagement ◽  
Connection Strength ◽  
Information Propagation ◽  
Edge Strength ◽  
Strength Based ◽  
Network Propagation ◽  
Overlapping Community

The iterative propagation of information between nodes will strengthen the connection strength between nodes, and the network can evolve into different groups according to difference edge strength. Based on this observation, we present the user engagement to quantify the influences of users different propagation modes to network propagation, and construct weight network to simulate real social network, and proposed the community detection method in social networks based on information propagation and user engagement. Our method can produce different scale communities and overlapping community. We also applied our method to real-world social networks. The experiment proved that the network spread and the community division interact with each other. The community structure is significantly different in the network propagation of different scales.

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