scholarly journals Multiplex Network Analysis of the UK OTC Derivatives Market

2018 ◽  
Author(s):  
Marco Bardoscia ◽  
Ginestra Bianconi ◽  
Gerardo Ferrara
Keyword(s):  
Network Analysis ◽  
Otc Derivatives ◽  
Multiplex Network ◽  
The Uk

scholarly journals Multiplex network analysis of the UK over‐the‐counter derivatives market

2019 ◽  
Vol 24 (4) ◽  
pp. 1520-1544
Author(s):  
Marco Bardoscia ◽  
Ginestra Bianconi ◽  
Gerardo Ferrara
Keyword(s):  
Network Analysis ◽  
Over The Counter ◽  
Multiplex Network ◽  
The Uk

What Makes a Tweet Fly? Analysis of Twitter Messaging at Four Infection Control Conferences

2017 ◽  
Vol 38 (11) ◽  
pp. 1271-1276 ◽  
Author(s):  
Brett G. Mitchell ◽  
Philip L. Russo ◽  
Jonathan A. Otter ◽  
Martin A. Kiernan ◽  
Landon Aveling
Keyword(s):  
Network Analysis ◽  
Infection Control ◽  
Infection Prevention ◽  
Binary Logistic Regression ◽  
Cross Sectional Study ◽  
Infection Prevention And Control ◽  
Binary Logistic Regression Model ◽  
Cross Sectional ◽  
Activity Networks ◽  
The Uk

OBJECTIVETo examine tweeting activity, networks, and common topics mentioned on Twitter at 4 international infection control and infectious disease conferences.DESIGNA cross-sectional study.METHODSAn independent company was commissioned to undertake a Twitter ‘trawl’ each month between July 1, 2016, and November 31, 2016. The trawl identified any tweets that contained the official hashtags of the conferences for (1) the UK Infection Prevention Society, (2) IDWeek 2016, (3) the Federation of Infectious Society/Hospital Infection Society, and (4) the Australasian College for Infection Prevention and Control. Topics from each tweet were identified, and an examination of the frequency and timing of tweets was performed. A social network analysis was performed to illustrate connections between users. A multivariate binary logistic regression model was developed to explore the predictors of ‘retweets.’RESULTSIn total, 23,718 tweets were identified as using 1 of the 2 hashtags of interest. The results demonstrated that the most tweets were posted during the conferences. Network analysis demonstrated a diversity of twitter networks. A link to a web address was a significant predictor of whether a tweet would be retweeted (odds ratio [OR], 2.0; 95% confidence interval [CI], 1.9–2.1). Other significant factors predicting a retweet included tweeting on topics such as Clostridium difficile (OR, 2.0; 95% CI, 1.7–2.4) and the media (OR, 1.8; 95% CI, 1.6–2.0). Tweets that contained a picture were significantly less likely to be retweeted (OR, 0.06; 95% CI, 0.05–0.08).CONCLUSIONTwitter is a useful tool for information sharing and networking at infection control conferences.Infect Control Hosp Epidemiol 2017;38:1271–1276

scholarly journals Network analysis of the social and demographic influences on name choice within the UK (1838-2016)

2018 ◽  
Author(s):  
Stephen Bush ◽  
Anna Powell-Smith ◽  
Tom C Freeman
Keyword(s):  
Network Analysis ◽  
Population Sample ◽  
Web Interface ◽  
Media Freedom ◽  
Freedom Of Movement ◽  
The Social ◽  
The Uk ◽  
Globally Distributed ◽  
Case Basis ◽  
Made In

Chosen names reflect a society’s changing values, aspirations and cultural diversity. Vogues in name usage can be easily shown on a case by case basis, by plotting the rise and fall in their popularity over time. However, individual name choices are not made in isolation and trends in naming are better understood as group-level phenomena. Here we use network analysis to examine onomastic (name) datasets in order to explore the influences on name choices within the UK over the last 170 years. Using a large representative sample of approximately 22 million chosen forenames from England and Wales given between 1838 and 2014, along with a complete population sample of births registered between 1996 and 2016, we demonstrate how trends in name usage can be visualised as network graphs.By exploring the structure of these graphs, various patterns of name use become readily apparent, which can be interpreted in the context of historic events, such as known waves of migration. In general, we show that the topology of network graphs can reveal naming vogues, and that naming vogues are a reflection of social and demographic changes.Many name choices are consistent with a self-correcting feedback loop, whereby rarer names become common because there are virtues perceived in their rarity, yet with these perceived virtues lost upon increasing commonality. Towards the present day, the comparatively greater range of media, freedom of movement, and ability to maintain globally-distributed social networks increases the number of possible names, but also ensures they may more quickly be perceived as commonplace. Consequently, contemporary naming vogues are relatively short-lived with many name choices appearing a balance struck between recognisability and rarity.The data are available via an easy-to-use web interface at http://demos.flourish.studio/namehistory.

scholarly journals Supply Chain Partner Communication in a Managed Programme in the UK Water Industry: A Case Study with Social Network Analysis

2019 ◽  
Vol 16 (21) ◽  
pp. 4211 ◽  
Author(s):  
Qing Li ◽  
Shengqiao Wang ◽  
Nicky Shaw ◽  
Victor Shi
Keyword(s):  
Supply Chain ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Water Industry ◽  
Partner Communication ◽  
Cross Sectional ◽  
Water Utility ◽  
Supply Chain Networks ◽  
The Uk

The water industry in every country aims to effectively and efficiently provide water with satisfactory quality in a sustainable and environmentally friendly manner. To this end, it is critical to achieve effective communication among the partners in water supply chain networks. In this paper, we focus on one of the UK’s largest water utility companies and its eight main contractors and analyze the factors influencing partner and network communication in a managed programme of their asset supply chain. We employ social network analysis to conduct the cross-sectional and longitudinal analysis of partner communication. Factors found to influence the communication network are grouping of projects within the programme, individual’s organisational affiliation, status, tenure, elapsed time through the programme lifecycle, and co-location. Our contributions to practice include demonstrating water programme management factors that influence communication and trust and how social network analysis can better inform them about intra- and interorganisational relationships. Moreover, the methodology introduced in this study may be applied to water management in other parts of the world.

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