Yasmin H. Said

RETRACTED: Social networks of author--coauthor relationships

Social network analysis has proven to be a useful tool in analysis of many situations. We begin by giving an overview of social network analysis. We then illustrate the concepts by examining the social networks of co-authors of scholarly publications. Scholarly publication is in many ways the lifeblood of academic institutions and there are strong incentives, both in terms of prestige and financial compensation, for faculty members to publish. Different disciplines and individuals have evolved distinguishable mechanisms for coping with the publication pressures. We examine the co-authorship networks of a number of prominent scholars. Based on the clustering within the co-author social network, we distinguish several styles of co-authorship including solo models (no co-authors), mentor models, entrepreneurial models, and team models. We conjecture that certain styles of co-authorship lead to the possibility of group-think, reduced creativity, and the possibility of less rigorous reviewing processes.

Quantitative Assessments of Alcohol-Related Outcomes

Caffeine, nicotine, and alcohol are addictive substances widely used and easily available in most of the world. The negative consequences of tobacco use have been widely discussed in both the technical medical literature and the popular press. The quantitative consequences of tobacco use in terms of both individual mortality and societal costs are relatively well-documented and generally understood. Negative consequences of tobacco use are primarily manifested in chronic diseases visited upon the individual user. Most people intuitively recognize there are negative consequences of alcohol use as well, but the quantitative assessment of alcohol use is less well-appreciated. This is perhaps because the negative consequences are both chronic and acute and sometimes indirect, being visited upon individuals who are not the users of alcohol. Because of the indirect, “innocent party” nature of these consequences, there is a much more significant societal benefit to public health by examining the alcohol-related outcomes in a more quantitative fashion. The majority of people who use alcohol (approximately 50% of the United States’ population) use it without harming themselves or others. It is argued that moderate alcohol intake is beneficial in preventing some forms of heart disease. However, excess alcohol intake may be associated with malnutrition and may induce one of at least 60 distinct alcoholrelated pathologies. As with tobacco, such chronic pathologies directly affect only the alcohol user. However, acute outcomes of excessive alcohol intake may result in assault, murder, suicide, domestic violence, child abuse, sexual assault (including infections with sexually transmitted diseases), deaths and injuries involved with driving while intoxicated, and other forms of intentional and unintentional injuries. Negative outcomes are not just associated with increased mortality, but also increased morbidity—that is, loss of quality of life because of living with a disability or disease. An attempt to quantify both mortality and years of life lived with reduced quality of life has resulted in the concept of disability-adjusted life years (DALYs). While some risk factors have high mortality rates and thus result in short amounts of time of living with reduced quality of life, alcoholrelated diseases and injuries can result in long periods of life lived with severely reduced quality of life. Thus, capturing the impact of reduced quality of life in a public health framework is essential to a rational resource allocation process.

Retraction notice to "Social networks of author-coauthor relationships" [Comput. Statist. Data Anal. 52 (4) 2177-2184]

This article has been retracted at the request of the Editor in Chief and co-Editors, as it contain portions of other authors’ writings on the same topic in other publications, without sufficient attribution to these earlier works being given. The principal authors of the paper acknowledged that text from background sources was mistakenly used in the Introduction without proper reference to the original source. Specifically, the first page and a half of the article (pp. 2177–2178) contain together excerpts from Wikipedia (first paragraph), Wasserman and Faust’s ‘‘Social Network Analysis: Methods and Applications’’ (pp. 17–20) ISBN 10: 0521387078/0-521-38707-8 ISBN 13: 9780521387071 Publication Date: 1994, and W. de Nooy, A. Mrvar, and V. Bategelj’s ‘‘Exploratory Social Network Analysis with Pajek’’ (pp. 31, 36, 123, and 133) ISBN 10: 0521602629/0-521-60262ISBN 13: 9780521602624 Publication Date: 2005. The scientific community takes a strong view on this matter and apologies are offered to readers of the journal that this was not detected during the submission process. One of the conditions of submission of a paper for publication is that authors declare explicitly that their work is original and has not appeared in a publication elsewhere. The re-use of material, without appropriate reference, even if not known to the authors at the time of submission, breaches our publishing policies.

Predicting Edges and Vertices in a Network

To achieve a composite integration of automation system on underground ore transport information and enhance the production efficiency and the management level of the mine, this article uses image processing and pattern recognition technology to establish the information platform of the relationships of crew, vehicles, mountain Pulse number and output.This paper addresses missing edges and vertices in a network. We discuss interchangeability and duality between vertices and edges in a graph. We use covariate information associated with vertices to estimate the probability of missing edges; likewise, we use covariate information associated with edges to estimate the probability of missing vertices. In order to predict missing vertices, we apply the line graph transformation, which converts edges to vertices and vertices to edges. The probability of an edge is obtained by taking the inner product of the vectors of covariates. Moreover, we have extended the methodology of predicting two edges (dyadic ties) to predict edge

A Social Network Model of Alcohol Behaviors

Alcohol use is a major cause of morbidity and mortality in all areas of the world (Ezzati et al., 2002). In the developed world alcohol consumption is one of the primary risk factors for the burden of disease; it ranks as the second leading risk factor (after tobacco) for all disease and premature mortality in the United States (Said and Wegman, 2007). Alcohol is such a potent risk factor because it is widely used, inexpensive, and causes both acute and chronic consequences. A major difficulty in developing and assessing alcohol-related public health interventions is that drinking behaviors and their consequences form, in effect, a complex ecological system of individual behaviors embedded in socio-cultural settings and interactions that are distributed over space and time. Interventions focused on any specific aspect of this system (e.g., drinking and driving) may result in unintended consequences (e.g., a potential increase in domestic violence as the result of high volume drinkers spending more time at home due to the anti-drinking-driving programs). Another example of unintended consequences is alcohol-related promiscuous behavior resulting in infections with sexually transmitted diseases (STD) and Human Immunodeficiency Virus (HIV).

Estimating Cyclic and Geospatial Effects of Alcohol Usage in a Social Network Directed Graph Model

Alcohol use and abuse contributes to both acute and chronic negative health outcomes and represents a major source of mortality and morbidity in the world as a whole (Ezzati et al., 2002) and in the developed world, such as the United States, where alcohol consumption is one of the primary risk factors for the burden of disease. It ranks as a leading risk factor after tobacco for all disease and premature mortality in the United States (Rehm et al., 2003; Said and Wegman, 2007). In a companion piece to this paper, Wegman and Said (2009) outline a graph-theoretic agent based simulation tool that accommodates the temporal and geospatial dimensions of acute outcomes. In order to complement that modeling paper, this paper focuses on methods to exploit temporal and spatial data with the idea of calibrating the model to include these effects. The model developed inWegman and Said (2009) incorporates a social network component. The overall goal of the model is not just to simulate known data, but to provide a policy tool that would allow decision makers to examine the feasibility of alcohol-related interventions. Such interventions are designed to reduce one or more acute outcomes such as assault, murder, suicide, sexual assault, domestic violence, child abuse, and DWI-related injuries and deaths. By adjusting conditional probabilities, the effect of interventions can be explored without actually introducing major societal policy actions.

Geospatial distribution of alcohol-related violence in Northern Virginia

This paper establishes a modeling framework for alcohol abuse that allows evaluation of interventions meant to reduce adverse effects of alcohol overuse without the financial, social and other costs of imposing interventions that are ultimately ineffective (or even simply not cost effective). The framework is ecological (individual agents and their activities are represented), stochastic (neither individual behavior nor consequences of interventions are certain) and flexible.