Sexual partners’ referral for HIV testing through social networking platforms: A social network analysis (Preprint)

2021 ◽  
Author(s):  
Piao-Yi Chiou ◽  
Chien-Ching Hung ◽  
Chien-Yu Chien
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Networking ◽  
Hiv Testing ◽  
Sexual Partners ◽  
Hiv Positive ◽  
Network Graph ◽  
Hiv Test ◽  
The Social

BACKGROUND Men who have sex with men (MSM) who undergo HIV voluntary counselling and testing (VCT) usually self-identify as having many sexual partners and as being exposed to risky sexual networks. Limited research discusses the application of motivative interviews and convenience referral platforms for MSM to facilitate the referral of sexual partners to HIV testing. The social network analysis (SNA) of such referral strategy remains unclear. OBJECTIVE To evaluate the effects of sexual partners’ referral through the social networking platforms for HIV testing and the test results after having elicited interviews with MSM, compare the different characteristics and risk behaviors of the subgroups, and to explore the unknown sexual affiliations through visualizing and quantifying the social network graph. METHODS This is a cohort study design. Purposeful sampling was used to recruit the index subjects at a community HIV screening station that is frequented by MSM in Taipei City on Friday and Saturday nights. Respondent-driven sampling was used to recruit the sexual partners. Partner-elicited interviews were conducted by trained staff before the VCT to motivate MSM to become the referrer to refer sexual partners via the Line application (app) or to disclose the account and profile on the relevant social networking platforms. The rapid HIV test was delivered to the referred sexual partners and the recruitment process continued in succession until leads were exhausted. RESULTS After the interviews, 28.2% (75/266) MSM were successfully persuaded to be index subjects in the first wave, referring 127 sexual partners via the Line app for the rapid HIV testing, and disclosing 40 sexual partners. The index subjects and the tested sexual partners exhibited higher numbers of sexual partners (F = 3.83, P = .023), higher frequencies of anal intercourse (F = 10.10, P < .001), and higher percentages of those who had not previously received HIV testing (x2 = 6.106, P = .047) when compared to the subjects without referrals. The newly HIV-seropositivity rate of tested sexual partners was 2.4%, which was higher than the other two groups. The SNA discovered four types of sexual affiliation, namely chain, Y, star, and complicated type. The complicated type had the most HIV-positive nodes. There were 26.87% (43/160) of the HIV-negative sexual partners who had sexual affiliations with HIV-positive nodes; 40% of them (10/25) were untested sexual partners, who had directly sexual affiliation with HIV-positive node. Four transmission bridge was found in the network graph. CONCLUSIONS Partner-elicited interviews can effectively promote the referral or disclosure sexual partners via social networking platforms for HIV testing and HIV case finding, and can reveal unknown sexual affiliations of MSM that can facilitate the development of a tailored prevention program.

scholarly journals Implementation and Analysis of Social Network Graph in Interpersonal Network

2020 ◽  
Vol 13 (2) ◽  
pp. 5
Author(s):  
Akshay Tripathi ◽  
Ankush Kumar Gaur ◽  
Sweta Sri
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Adjacency Matrix ◽  
Graphical Model ◽  
Farm Animals ◽  
Disease Spread ◽  
Network Graph ◽  
Graph Properties ◽  
The Social

Social graph describes the graphical model of users and how they are related to each other online. Social network consists of a set of nodes (sometimes referred to as actors or vertices in graph theory) connected via some type of relations which are known as edges. Actors are the smallest unit of the network. It can be Persons, Organizations, and Families etc. Relations can be of many types such as directed, undirected, and weighted. Social network analysis consists of two phases. One is data collection phase and another is analysis phase. Data is collected with the help of surveys, Social sites such as face book, LinkedIn. We first input the user information in form of two dimensional matrices. Then we construct a graph based on the relationships among users from adjacency matrix. We can draw a directed graph or a simple graph based on the user input information from adjacency matrix. After analyzing the graph properties based on degree of node, centrality and other parameters we will give effective solution. There are many applications of analyzing social network for example examine a network of farm animals, to analyze how disease spread from one cow to another, discover emergent  communities of interest among faculty at various universities, Some public sector uses include development of leader engagement strategies, analysis of individual and group engagement and media use, and community-based problem solving etc. Social network analysis is used widely in the social and behavioral sciences, as well as in economics, marketing, and industrial engineering. The social network perspective focuses on the relationships among social entities and is an important addition to standard social and behavioral research which is primarily concerned with attributes of the social units.

Emergency Management Training and Social Network Analysis

2015 ◽  
pp. 273-289 ◽  
Author(s):  
Knut Ekker
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Networking ◽  
Empirical Data ◽  
Training Tool ◽  
European Regional Development Fund ◽  
Emergency Personnel ◽  
The Social ◽  
The Rich

The chapter first presents a background review of the application of computer technology in simulations of natural hazard situations. The chapter then presents the efforts of researchers at Mid Sweden University and Nord-Trøndelag University College to build a comprehensive emergency training tool with funding from the Interreg/EU ERDF (European Regional Development Fund). The main part of the chapter reports empirical data from this project GSS (Gaining Security Symbiosis). The project developed the tool for training emergency personnel (police, fire, ambulance, and local officials) in handling crises in the border region between Norway and Sweden. The Web-based software incorporated complex scenarios that the emergency personnel had to contend with during 3-hour training sessions. The participants included employees at the operator, tactical, and strategic level of the organizations. The training tool recorded all communications among participants which primarily was text based. The rich data source was analyzed “on-the-fly” with the software from the R Project for statistical computing and the SNA package (Social Network Analysis package). The statistical software provided detailed graphs of the social networking of communications among the participants on both sides of the national border in central Scandinavia. The chapter concludes with a presentation of ideas towards using the social networking data as input into simulation models based on system dynamics. The empirical data from the project will naturally provide data for future training sessions. A planned future model of the comprehensive training tool—netAgora—will use experiential data in a Virtual Responder component in the training sessions of emergency personnel.

Predicting investor funding behavior using crunchbase social network features

2016 ◽  
Vol 26 (1) ◽  
pp. 74-100 ◽  
Author(s):  
Yuxian Eugene Liang ◽  
Soe-Tsyr Daphne Yuan
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Link Prediction ◽  
Social Relationship ◽  
Jaccard Coefficient ◽  
Network Graph ◽  
Content Type ◽  
The Social ◽  
Relationship Factors

Purpose – What makes investors tick? Largely counter-intuitive compared to the findings of most past research, this study explores the possibility that funding investors invest in companies based on social relationships, which could be positive or negative, similar or dissimilar. The purpose of this paper is to build a social network graph using data from CrunchBase, the largest public database with profiles about companies. The authors combine social network analysis with the study of investing behavior in order to explore how similarity between investors and companies affects investing behavior through social network analysis. Design/methodology/approach – This study crawls and analyzes data from CrunchBase and builds a social network graph which includes people, companies, social links and funding investment links. The problem is then formalized as a link (or relationship) prediction task in a social network to model and predict (across various machine learning methods and evaluation metrics) whether an investor will create a link to a company in the social network. Various link prediction techniques such as common neighbors, shortest path, Jaccard Coefficient and others are integrated to provide a holistic view of a social network and provide useful insights as to how a pair of nodes may be related (i.e., whether the investor will invest in the particular company at a time) within the social network. Findings – This study finds that funding investors are more likely to invest in a particular company if they have a stronger social relationship in terms of closeness, be it direct or indirect. At the same time, if investors and companies share too many common neighbors, investors are less likely to invest in such companies. Originality/value – The author’s study is among the first to use data from the largest public company profile database of CrunchBase as a social network for research purposes. The author ' s also identify certain social relationship factors that can help prescribe the investor funding behavior. Authors prediction strategy based on these factors and modeling it as a link prediction problem generally works well across the most prominent learning algorithms and perform well in terms of aggregate performance as well as individual industries. In other words, this study would like to encourage companies to focus on social relationship factors in addition to other factors when seeking external funding investments.

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 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

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.  

scholarly journals Social Network Analysis Visualization

2015 ◽  
pp. 257-270
Author(s):  
Mohana Shanmugam ◽  
Yusmadi Yah Jusoh ◽  
Rozi Nor Haizan Nor ◽  
Marzanah A. Jabar
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Online Social Network ◽  
Future Research ◽  
Research Gaps ◽  
The Social ◽  
Network Studies ◽  
Academic Study

The social network surge has become a mainstream subject of academic study in a myriad of disciplines. This chapter posits the social network literature by highlighting the terminologies of social networks and details the types of tools and methodologies used in prior studies. The list is supplemented by identifying the research gaps for future research of interest to both academics and practitioners. Additionally, the case of Facebook is used to study the elements of a social network analysis. This chapter also highlights past validated models with regards to social networks which are deemed significant for online social network studies. Furthermore, this chapter seeks to enlighten our knowledge on social network analysis and tap into the social network capabilities.

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