scholarly journals Social network analysis and community detection on spread of COVID-19

2021 ◽  
Vol 16 (1) ◽  
pp. 37-52
Author(s):  
Ashani Nuwanthika Wickramasinghe ◽  
Saman Muthukumarana
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Community Detection ◽  
Label Propagation ◽  
Adjusted Rand Index ◽  
Exponential Random Graph Models ◽  
Normalized Mutual Information ◽  
Information Score ◽  
The Impact

This paper explains the epidemic spread using social network analysis, based on data from the first three months of the 2020 COVID-19 outbreak across the world and in Canada. A network is defined and visualization is used to understand the spread of coronavirus among countries and the impact of other countries on the spread of coronavirus in Canada. The degree centrality is used to identify the main influencing countries. Exponential Random Graph Models (ERGM) are used to identify the processes that influence link creation between countries. The community detection is done using Infomap, Label propagation, Spinglass, and Louvain algorithms. Finally, we assess the community detection performance of the algorithms using adjusted rand index and normalized mutual information score.

scholarly journals Investigation of Rater Effects Using Social Network Analysis and Exponential Random Graph Models

2017 ◽  
Vol 78 (3) ◽  
pp. 430-459 ◽  
Author(s):  
Iasonas Lamprianou
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
High Stakes ◽  
Rating Data ◽  
New Approach ◽  
Exponential Random Graph ◽  
Rater Severity

It is common practice for assessment programs to organize qualifying sessions during which the raters (often known as “markers” or “judges”) demonstrate their consistency before operational rating commences. Because of the high-stakes nature of many rating activities, the research community tends to continuously explore new methods to analyze rating data. We used simulated and empirical data from two high-stakes language assessments, to propose a new approach, based on social network analysis and exponential graph models, to evaluate the readiness of a group of raters for operational rating. The results of this innovative approach are compared with the results of a Rasch analysis, which is a well-established approach for the analysis of such data. We also demonstrate how the new approach can be practically used to investigate important research questions such as whether rater severity is stable across rating tasks. The merits of the new approach, and the consequences for practice are discussed.

Aspects of Various Community Detection Algorithms in Social Network Analysis

2019 ◽  
pp. 327-341
Author(s):  
Nicole Belinda Dillen ◽  
Aruna Chakraborty
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Community Detection ◽  
Overlapping Community Detection ◽  
Detection Algorithms ◽  
Novel Approach ◽  
Recent Developments ◽  
Overlapping Community ◽  
Related Individuals

One of the most important aspects of social network analysis is community detection, which is used to categorize related individuals in a social network into groups or communities. The approach is quite similar to graph partitioning, and in fact, most detection algorithms rely on concepts from graph theory and sociology. The aim of this chapter is to aid a novice in the field of community detection by providing a wider perspective on some of the different detection algorithms available, including the more recent developments in this field. Five popular algorithms have been studied and explained, and a recent novel approach that was proposed by the authors has also been included. The chapter concludes by highlighting areas suitable for further research, specifically targeting overlapping community detection algorithms.

Measuring the Impact of Social Media

2011 ◽  
pp. 24-36 ◽  
Author(s):  
Kimiz Dalkir
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Evaluation Method ◽  
Real Life ◽  
Work Processes ◽  
The Social ◽  
Post Test ◽  
The Impact

This chapter focuses on a method, social network analysis (SNA) that can be used to assess the quantity and quality of connection, communication and collaboration mediated by social tools in an organization. An organization, in the Canadian public sector, is used as a real-life case study to illustrate how SNA can be used in a pre-test/post-test evaluation design to conduct a comparative assessment of methods that can be used before, during and after the implementation of organizational change in work processes. The same evaluation method can be used to assess the impact of introducing new social media such as wikis, expertise locator systems, blogs, Twitter and so on. In other words, while traditional pre-test/post-test designs can be easily applied to social media, the social media tools themselves can be added to the assessment toolkit. Social network analysis in particular is a good candidate to analyze the connections between people and content as well as people with other people.

Introduction

2020 ◽  
pp. 1-29
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Structure ◽  
Research Methods ◽  
The Other ◽  
Traditional Research ◽  
The Social ◽  
Social Realm ◽  
The Impact

The traditional research approaches common in different disciplines of social sciences centered around one half of the social realm: the actors. The other half are the relations established by these actors and forming the basis of “social.” The social structure shaped by these relations, the position of the actor within this structure, and the impact of this position on the actor are mostly excluded by the traditional research methods. In this chapter, the authors introduce social network analysis and how it complements the other methods.

scholarly journals Community detection and social network analysis based on the Italian wars of the 15th century

2020 ◽  
Vol 113 ◽  
pp. 25-40
Author(s):  
J. Fumanal-Idocin ◽  
A. Alonso-Betanzos ◽  
O. Cordón ◽  
H. Bustince ◽  
M. Minárová
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Community Detection ◽  
15Th Century ◽  
Italian Wars

Social Network Analysis in Child and Adolescent Physical Activity Research: A Systematic Literature Review

2020 ◽  
Vol 17 (2) ◽  
pp. 250-260 ◽  
Author(s):  
Tyler Prochnow ◽  
Haley Delgado ◽  
Megan S. Patterson ◽  
M. Renée Umstattd Meyer
Keyword(s):  
Physical Activity ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Children And Adolescents ◽  
Child And Adolescent ◽  
Adolescent Physical Activity ◽  
Final Sample ◽  
Network Composition ◽  
The Impact

Background: Regular physical activity (PA) has many benefits for children and adolescents, yet many do not meet PA recommendations. Social context is important for promoting or discouraging PA among children and adolescents. This review aimed to identify social network variables related to PA among children and adolescents. Methods: A systematic review of the literature was conducted in September 2018 using PsycINFO, MEDLINE, PubMed, and Web of Science. Included articles needed to (1) be focused on children (aged 5–11 y) or adolescents (aged 12–17 y), (2) include a measure of PA, (3) include a measure of egocentric or sociocentric social connection in which alters were nominated, and (4) perform an analysis between network data and PA. Results: A search of 11,824 articles was refined to a final sample of 29 articles. Social network themes and concepts such as homophily, centrality, and network composition were related to child and adolescent PA behavior across the literature. Conclusions: The impact of an individual’s social network is evident on their PA behaviors. More research is needed to examine why these networks form in relation to PA and how interventions can utilize social network analysis to more effectively promote PA, especially in underserved and minority populations.

Community Detection Methods in Social Network Analysis

2014 ◽  
Vol 20 (1) ◽  
pp. 250-253 ◽  
Author(s):  
Andry Alamsyah ◽  
Budi Rahardjo ◽  
. Kuspriyanto
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Community Detection ◽  
Detection Methods
Sign in / Sign up

Export Citation Format

Share Document