scholarly journals Who Is the Keyman? Integrating Two-Stage DEA and Social Network Analysis to Evaluate Operational and Environmental Efficiency in the Semiconductor Industry

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Tzu-Yi Fang
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Semiconductor Industry ◽  
Operational Efficiency ◽  
Environmental Efficiency ◽  
Decision Makers ◽  
Network Graph ◽  
Two Stage ◽  
A Company

The study considers the semiconductor industry’s business process to be made up of two stages. In the business development process, a company generates profit and consumes energy while polluting the environment. After the two-stage data envelopment analysis approach was employed for calculating the operational efficiency and environmental efficiency, social network analysis was used to compare the manner in which the internal advantages or individual process factors of 28 semiconductor companies contribute to efficiency. A network graph was plotted to visualize relationships, with each node in the network graph representing a company. This graph was plotted to help decision-makers and manufacturers understand information communication among companies and the importance of the company in the network and help companies develop a mutual understanding to improve operational efficiency. The results of the study indicated that having an efficient company does not necessarily mean that the company plays a key role in the entire industry. The results provide decision-makers with references for improvements and information for learning from these references.

scholarly journals Basic Criteria, Models, and Indicators of Intersectoral Collaboration in Health Promotion: A Scoping Review

2021 ◽  
Author(s):  
Mohammad Reza Amir Esmaili ◽  
Behzad Damari ◽  
Ahmad Hajebi ◽  
Noora Rafiee ◽  
Reza Goudarzi ◽  
...  
Keyword(s):  
Health Promotion ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Scoping Review ◽  
Web Of Science ◽  
Decision Makers ◽  
Macro Level ◽  
Intersectoral Collaboration ◽  
Micro Level

Background: In this study, the basic criteria, models, and indicators of intersectoral collaboration in health promotion were investigated to facilitate the implementation of collaboration. Methods: This scoping review was conducted using datasets of Embase, Web of Science, Scopus, and PubMed, and search engines of Google, Google Scholar, and ProQuest. Results: 52 studies were included, and 32 codes in Micro, Meso, and Macro level, were obtained. Micro-level criteria had the highest frequency. Among the models used in the reviewed studies, social network analysis, Diagnosis of Sustainable Collaboration, Bergen, and logic models had the highest frequency. Among the indicators studied, the number of participants and the level of collaboration as well as its sustainability were the most frequent indicators. Conclusion: The findings identified the most important and widely used criteria, models, and indicators of intersectoral collaboration in health promotion which can be useful for decision-makers and planners in the domain of health promotion, in designing, implementing, and evaluating collaborative programs.

Abstract P214: Social Network Analysis of a Community Aphasia Group Pre- And Post-Covid: Implications for Future Planning

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Tom Sather ◽  
Anna Livera
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Time Frame ◽  
Directed Network ◽  
Negative Consequences ◽  
Network Graph ◽  
Future Planning ◽  
Network Characteristics ◽  
The Impact

Introduction: Among the many negative consequences of aphasia is an altered social network. Social network analysis supports an objective, quantitative evaluation of social networks among individuals with aphasia along with potential impacts of social programming and interventions on an individual’s social network. Social network analysis may also support better understanding of the impact of Covid on individuals with aphasia. Aims: This pilot evaluation utilized social network analysis via R to evaluate the social network characteristics of a community-based aphasia network across a 12-month pre-Covid period. Social network aphasia group data for a standard duration of time pre- and post-Covid were also compared to identify potential social implications of Covid in a population already at higher risk for reduced social interactions. This presentation will also provide fundamental concepts relevant to social network analysis for those interested in pursuing such analysis in further depth. Methods: Twelve months of pre-Covid aphasia group program attendance data were examined using the visNetwork R package. An additional six months of Covid-era time frame data were also analyzed.The primary relationship function of “ a attended b” (where a = individual participant and b = event/setting) was used in the analysis. Multiple social network characteristics were analyzed and displayed including node, edgeness, directionality, weight, and centrality indices across individuals with aphasia, care partners and community members and settings. Results and Conclusions: Network analysis reveals a directed network graph with primarily unidirectional relationships. There is an emergence of several aphasia group participant behavior types, both pre- and post-Covid, relevant for future planning including: communities of individuals who have similar behaviors in terms of type of event attendance; key individuals who are "heavy users" of various services in terms of frequency and breadth of event attendance; and peripheral users who use only one service. Post-Covid social network implications are discussed including supports to mitigate negative impacts of Covid on social network composition.

Relationships between public health faculty and decision makers at four governmental levels: a social network analysis

2018 ◽  
Vol 14 (3) ◽  
pp. 499-522 ◽  
Author(s):  
Nasreen S Jessani ◽  
Carly Babcock ◽  
Sameer Siddiqi ◽  
Melissa Davey-Rothwell ◽  
Shirley Ho ◽  
...  
Keyword(s):  
Public Health ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Decision Makers

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 Social Network Analysis and Churn Prediction in Telecommunications Using Graph Theory

Entropy ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. 753 ◽  
Author(s):  
Stefan M. Kostić ◽  
Mirjana I. Simić ◽  
Miroljub V. Kostić
Keyword(s):  
Graph Theory ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Churn Prediction ◽  
Network Graph ◽  
Network Analytics ◽  
Telecommunications Market ◽  
Market Saturation ◽  
Important Nodes

Due to telecommunications market saturation, it is very important for telco operators to always have fresh insights into their customer’s dynamics. In that regard, social network analytics and its application with graph theory can be very useful. In this paper we analyze a social network that is represented by a large telco network graph and perform clustering of its nodes by studying a broad set of metrics, e.g., node in/out degree, first and second order influence, eigenvector, authority and hub values. This paper demonstrates that it is possible to identify some important nodes in our social network (graph) that are vital regarding churn prediction. We show that if such a node leaves a monitored telco operator, customers that frequently interact with that specific node will be more prone to leave the monitored telco operator network as well; thus, by analyzing existing churn and previous call patterns, we proactively predict new customers that will probably churn. The churn prediction results are quantified by using top decile lift metrics. The proposed method is general enough to be readily adopted in any field where homophilic or friendship connections can be assumed as a potential churn driver.

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.

Social network analysis of mythology field

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sümeyye Akça ◽  
Müge Akbulut
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Decision Makers ◽  
Content Type ◽  
Centrality Metrics ◽  
Subject Categories ◽  
The Subject ◽  
Number Of Publications ◽  
Network Analysis Metrics

PurposeThe main purpose of the study is to detect, monitor the mythology field and make predictions of the development of it using social network analysis metrics. Mythology, which is the subject of many disciplines, is an area with extensive working potential. In addition to basic bibliometric indicators, the relationships of this field, which cannot be seen by other methods, were analyzed using measures such as centrality, between, eigenvector, modularity and silhouette coefficients.Design/methodology/approachIn this study, social network analysis of the field of mythology, which has an interdisciplinary structure, was made. Within the scope of the study, 28,370 publications were selected from the publications in the field of mythology in the Web of Science (WoS) citation database between 1900 and 2019 using the probability-based stratified sampling method (5%), and detailed analyzes were made on these publications. The aforementioned publications were analyzed in terms of publication and citation numbers, publication types, subject categories, keywords used, co-authorship, researchers with the highest number of publications, institutions and countries with the highest number of document co-citations.FindingsThe findings show that the field of mythology gathers around four main subjects (sociology, folklore, politics and anthropology). When interpreted in terms of centrality metrics in more detail, the symbiotic or complementary relationship between anthropology, folklore, politics, sociology and mythology can be easily observed.Originality/valueThe findings of this study are seen important for scientists, decision-makers and policymakers. In addition, the findings of the study can be used to create the curriculum of the field.

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