scholarly journals Empiricism and Theorizing in Epidemiology and Social Network Analysis

2011 ◽  
Vol 2011 ◽  
pp. 1-5 ◽  
Author(s):  
Richard Rothenberg ◽  
Elizabeth Costenbader
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Empirical Data ◽  
Transmission Dynamics ◽  
Scientific Creativity ◽  
New Information ◽  
Testing Theory ◽  
The Relationship ◽  
Insight Into

The connection between theory and data is an iterative one. In principle, each is informed by the other: data provide the basis for theory that in turn generates the need for new information. This circularity is reflected in the notion of abduction, a concept that focuses on the space between induction (generating theory from data) and deduction (testing theory with data). Einstein, in the 1920s, placed scientific creativity in that space. In the field of social network analysis, some remarkable theory has been developed, accompanied by sophisticated tools to develop, extend, and test the theory. At the same time, important empirical data have been generated that provide insight into transmission dynamics. Unfortunately, the connection between them is often tenuous and the iterative loop is frayed. This circumstance may arise both from data deficiencies and from the ease with which data can be created by simulation. But for whatever reason, theory and empirical data often occupy different orbits. Fortunately, the relationship, while frayed, is not broken, to which several recent analyses merging theory and extant data will attest. Their further rapprochement in the field of social network analysis could provide the field with a more creative approach to experimentation and inference.

scholarly journals FACTORS AFFECTING DISPOSITION EFFECT IN EQUITY INVESTMENT: A SOCIAL NETWORK ANALYSIS APPROACH

2020 ◽  
Vol 5 (3) ◽  
pp. 64-86
Author(s):  
K. Kajol ◽  
Prasita Biswas ◽  
Ranjit Singh ◽  
Sana Moid ◽  
Amit Kumar Das
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Stock Market ◽  
Influencing Factors ◽  
Social Trust ◽  
Complete Analysis ◽  
Disposition Effect ◽  
Factors Affecting ◽  
The Relationship

The study aims at identifying the factors influencing the disposition effect acting on equity investors and further identifying the relationship between the influencing factors. The study aims at conducting a complete analysis of the influencing factors along with measuring their impact on disposition effect using Social Network Analysis (SNA).The factors affecting disposition effect on investors were identified through the literature review. Experts’ opinions were sought for determining the relationship among the factors and finally, the importance of those factors was analyzed using Social Network Analysis (SNA). It was found that social trust, investor emotion are the two most important factors affecting the other factors of disposition effect and consequently disposition effect finally. Besides, mental accounting; regret aversion, trading intensity, trading volume, and portfolio performance strongly influence the effect of disposition on investors because of their higher in-degree and out-degree. Therefore, the policymakers need to impart training to the investors to understand the mechanism of the stock market so that they can evaluate their standing in the stock market which, in the long run, will be reflected in their investment behavior. 

scholarly journals Social Networks and Supply Chain Management in Fish Trade

SAGE Open ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 215824402093181
Author(s):  
Carmen Pedroza-Gutiérrez ◽  
Juan M. Hernández
Keyword(s):  
Supply Chain ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Real Life ◽  
Primary Data ◽  
Structured Interviews ◽  
Knowledge Based ◽  
The Relationship

This study aims to construct a theoretical framework to analyze the elements of the network structure and the relationship system within the seafood supply chain. The scope of the investigation is to evaluate how these elements influence the flow of products and the efficiency of the seafood supply chain and why these social interactions can create value and enhance competitive advantage. The model combines the resource- and knowledge-based view and the social network analysis applied to seafood supply chains. To demonstrate the application of the model, two theoretical examples and a real case study of the Mercado del Mar in Guadalajara, Mexico, are used. Primary data are obtained from semi-structured interviews, social network analysis metrics, and qualitative analysis. Findings are based on the analysis of theoretical examples and must be considered with caution. Nevertheless, the observations in the examples and case study provide new arguments to the relationship between the pattern of interrelationship and the efficiency of a supply chain. This study emphasizes the necessity of combining quantitative and qualitative analyses to understand and explain real-life supply networks.

Application of Computer System in the Study of Sociology

2014 ◽  
Vol 926-930 ◽  
pp. 1680-1683
Author(s):  
Ying Ming Xu ◽  
Shu Juan Jin
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Computer Algorithms ◽  
Useful Knowledge ◽  
Social Network Data ◽  
Analysis Task ◽  
Original Analysis ◽  
The Social ◽  
The Relationship

With the development of information technology, more and more data about social to be collected. If we can analyze them effectively, it will help people to understand sociological understanding, promoting the development of social science. But the increasing amount of data and analysis to put forward a huge challenge. Now the social networks have already surpassed the processing ability of the original analysis means, must use a more effective tool to complete the analysis task. The computer as a way of helping people from massive data to find the potential useful knowledge tools, play an important role in many fields. Social network analysis, also known as link mining, refers to the handling of the relationship between social network data in the computer method. In this paper, the methods of computer and the social network analysis was introduced in this paper and the computer algorithms are summarized in the application of social network analysis.

scholarly journals The Research of Social Network Analysis on College Students' Interactive Relations

2021 ◽  
Vol 15 (2) ◽  
pp. 49-59
Author(s):  
Chang Chen ◽  
Min Chen
Keyword(s):  
College Students ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Student Affairs ◽  
Interpersonal Relations ◽  
Interaction Network ◽  
Information Support ◽  
Limited Information ◽  
New Information

Nowadays the number of college students' suicides are increasing for the insufficient social support or poor interpersonal relations. Furthermore, not much attention has been concerned to students' interpersonal relations when handling student affairs and only very limited information about students' interaction network is available. This paper studies the peer network of college students by using the tool of social network analysis. And it aims to serve as instrumental support for students to foster and develop harmonious interpersonal relations. It offers new information for school counsellor to better handle student affairs and provides information support for the carrying out of moral and ideological guidance for students.

scholarly journals Improvised Music in Berlin 2012-13: A Brief Ethnographic Portrait

2016 ◽  
Vol 10 (2) ◽  
Author(s):  
Tom Arthurs
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Structure ◽  
Point Of View ◽  
Music Making ◽  
Improvised Music ◽  
Phd Thesis ◽  
Point Of Departure ◽  
Insight Into

This paper uses approaches from ethnography and Social Network Analysis to provide a brief insight into the practical, economic and social structure of Berlin’s Improvised Music scene during 2012 and 2013. The findings presented here address imbalances of gender and race, and highlight the (often difficult) financial reality of a life in Improvised Music. Audience, venues and performers are portrayed in order to provide an entry point for those unfamiliar with Improvised Music communities, and to offer an empirically researched point of departure for those already acquainted with such musicians and practices. This paper is an adaptation of parts of my PhD thesis “The Secret Gardeners: An Ethnography of Improvised Music in Berlin (2012-13),” which addresses the aesthetics, ideologies and practicalities of contemporary European Improvised Music-making from the point of view of 34 key practitioners and “expert” listeners.

scholarly journals Using Google Maps to Show the Pattern of International Co-Author Collaboration in Pharmaceutics

2018 ◽  
pp. 1-9
Keyword(s):  
Drug Delivery ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Graphical Representations ◽  
Deep Insight ◽  
Microsoft Excel ◽  
Google Maps ◽  
Future Studies ◽  
Insight Into

Objective: To understand international co-author collaboration in pharmaceutics and to visualize results by Google maps and social network analysis (SNA). Methods: Selecting 311 abstracts from the Medline based on keyword pharmaceutics [journal], we reported following features of pharmaceutics: (1) nation distribution across continents; (2) main keywords frequently displayed in papers; (3) the eminent author in pharmaceutics. We programmed Microsoft Excel VBA for extracting data from Medline. Google Maps and SNA Pajek software show graphical representations of pharmaceutics. Results: We found that (1) the most number of papers in nations are from U.S.(81, 16.05%) and Japan(34, 10.93%); (2) the most linked keywords are Pharmacokinetics and drug delivery; (3) the eminent authors are Muhammad Sohail Arshad(UK) and Takeshi Yokoo(Japan). Conclusion: Social network analysis provides wide and deep insight into relationships of entities we interested. The results drawn from Google maps can provide more information to future studies in academics.

scholarly journals Using Social Network Analysis to Understand the Effects of a Social Media-based Weight Loss Intervention Targeting Low-SES Adults (Preprint)

2020 ◽  
Author(s):  
Ran Xu ◽  
David Cavallo
Keyword(s):  
Social Support ◽  
Social Media ◽  
Weight Loss ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Self Efficacy ◽  
Network Characteristics ◽  
Using Data ◽  
The Relationship

BACKGROUND Obesity is a known risk factor for cardiovascular disease (CVD) risk factors including hypertension and type II diabetes. Although numerous weight-loss interventions have demonstrated efficacy, there is considerably less evidence about the theoretical mechanisms through which they work. Delivering lifestyle behavior change interventions via social media provides unique opportunities for understanding mechanisms of intervention effects. Server data collected directly from online platforms can provide detailed, real-time behavioral information over the course of intervention programs that can be used to understand how interventions work. OBJECTIVE The objective of this study was to demonstrate how social network analysis can facilitate our understanding of the mechanisms underlying a social-media based weight loss intervention. METHODS This study performed secondary analysis using data from a pilot study that delivered a dietary and physical activity intervention to a group of low-SES participants via Facebook. We mapped out participants’ interaction networks over the 12-week intervention period, and linked participants’ network characteristics (e.g. in-degree, out-degree and network constraint) to participants’ changes in theoretical mediators (i.e. dietary knowledge, perceived social support, self-efficacy) and weight loss using regression analysis. This study also performed mediation analyses to explore how the effects of social network measures on weight loss could be mediated by the aforementioned theoretical mediators. RESULTS 47 participants from two waves completed the study and were included in the analysis. We found that participants creating posts, comments and reactions predicted weight-loss (β=-.94, P=.042); receiving comments positively predicted changes in self-efficacy (β=7.81, P=.009); the degree to which one’s network neighbors are tightly connected with each other weakly predicted changes in perceived social support (β=7.70, P=.08). In addition, change in self-efficacy mediated the relationship between receiving comments and weight-loss (Indirect effect=-.89, P=.017). CONCLUSIONS Our analyses using data from this pilot study have linked participants’ network characteristics with changes in several important study outcomes of interest, such as self-efficacy, social support and weight. Our results point to the potential of using social network analysis to understand the social processes and mechanisms through which online behavioral interventions affects participants’ psychological and behavioral outcomes. Future studies are warranted to validate our results and further explore the relationship between network dynamics and study outcomes in similar and larger trials.

scholarly journals Exploratory Experiment on Co-Authorship Network using Social Network Analysis Metrics and Measures

2018 ◽  
Vol 7 (4.35) ◽  
pp. 782
Author(s):  
Pritheega Magalingam ◽  
Ganthan Narayana Samy ◽  
Nurazean Maarop ◽  
Wan Nazirul Hafeez Wan Safie ◽  
Muhammad Khairul Rijal ◽  
...  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Single Measure ◽  
Research Issues ◽  
Structure Topology ◽  
Relationship Analysis ◽  
Exploratory Experiment ◽  
Centrality Analysis ◽  
The Relationship

This paper contributes in understanding and gaining meaningful insight about the relationship among the scientist in the co-authorship network using social network analysis. We argue that the relationship analysis is not always a straightforward process. In the past one single measure, for example, the egocentric or centrality measure was used to describe the scientific collaboration patterns separately. In this paper, various analysis such as centrality analysis, ego network, community detection, largest clique and word frequency have been used to examine and interpret the collaboration among the authors. This research is not dominated by known researchers but involves an overall exploration of the network. Our research is mainly guided by the creation of research issues, assessing the type of dataset and the objectives for presenting the co-authorship relationships. It is important to identify the motive of the selected measures in order to achieve the predefined objective. Specific methodology and procedures are designed to solve each research issue respectively. This study reveals that the network interpretation should not be solely based on one network measure, but an explorative analysis results need to be considered because it allows exploring the hidden information through the changes in the network structure, topology patterns and nodes’ position.

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