1,720,973 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Examining the Role of Social Resources in Diabetes Control among Middle-Aged and Older Adults
No full textDiabetes is a rapidly growing health issue in the United States and across the globe, and is currently the seventh leading cause of death in the United States. Uncontrolled diabetes can lead to other health complications, including coronary heart disease, stroke, kidney disease, vision loss, and Alzheimer’s disease. Diabetes also attributes to a large financial burden in the United States, costing an estimated $245 billion among individuals diagnosed with diabetes in 2012 and a 41 percent increase from 2007. Blood glucose control is essential to reducing diabetes complications and related health care costs. Social resources are central to adherence of these self-management practices, particularly in middle-aged and older adults. Past research has examined the effect of social resources on health behaviors and health outcomes, but little has been done to examine the role of chronic stress on this relationship. Chronic stress is important to diabetes control because stress can impair an individual’s ability to perform diabetes self-management behaviors. The purpose of this research was to fully identify: 1.) predictors of four diabetes control typologies, 2.) if chronic stress mediates the relationship between social embeddedness and diabetes control, and 3.) whether perceived social support moderates the relationship between chronic stress and diabetes control. Data from the 2006-2012 waves of the Health and Retirement Study, a nationally-representative study of adults in the United States, was utilized for these analyses. Study 1 found that perceived diabetes control predicted objective diabetes control. Multinomial logistic regression was employed to determine that age, race, income, self-rated health, perceived control over health, presence of ADLs and IADLs, duration of diabetes, restless sleep, smoking status, and taking oral medication and insulin to treat diabetes were significant predictors of at least one of the four diabetes control typologies, 1.) truly controlled, 2.) falsely controlled, 3.) falsely uncontrolled, and 4.) truly uncontrolled. The results of Study 1 suggest that other factors are associated with the disconnect between perceived and objective diabetes control. Study 2 found limited evidence of a relationship between social embeddedness and 1.) perceived and 2.) objective diabetes control. Generalized structural equation modeling was used to examine the mediating effect of 1.) number of chronic stressors and 2.) perceived stress on the relationship between social embeddedness and both types of diabetes control. One social embeddedness factor, contact with children through meeting in person and speaking on the phone, was fully mediated by perceived stress in its relationship with perceived diabetes control. However, perceived stress did not mediate the association between this social embeddedness factor and objective diabetes control. The results of Study 2 suggest that social embeddedness does not impact diabetes control in the presence of chronic stress, but that support from a social network may. Study 3 examined the relationship between perceived stress and five diabetes control outcomes, 1.) perceived diabetes control, 2.) objective diabetes control, 3.) use of oral medication to treat diabetes, 4.) use of insulin to treat diabetes, and 5.) insulin compliance based on doctor’s recommendation. This study also explored the moderating effect of perceived social support on the relationship between perceived stress and the five diabetes control outcomes. Overall, the findings from Study 3 suggest that perceived negative social support in the presence of high stress may hinder diabetes control and control-related behaviors, and that total social support from a spouse in the presence of high stress was predictive of insulin compliance. The project ultimately illustrated how perceptions of stress and support may impact perceptions of diabetes control and control-related behaviors, but not objective control. However, results of this study should be interpreted with caution because many of the psychosocial measures analyzed were not from validated survey instruments. Overall, future research must focus on how perceptions, whether of control, stress, or support, impact diabetes-related behaviors, and ultimately objective diabetes control. Public health programming can help to improve accurate perceptions of diabetes control by strengthening access to social resources and mitigating the impact of chronic stressors.Public Healt
Graph-Based Approach: Bridging Insights from Structured and Unstructured Data
No full textGraph-based methodologies provide powerful tools for uncovering intricate relationships and patterns in complex data, enabling the integration of structured and unstructured information for insightful decision-making across diverse domains. Our research focuses on constructing graphs from structured and unstructured data, demonstrating their applications in healthcare and power systems.
In healthcare, we examine how social networks influence the attitudes of hemodialysis patients toward kidney transplantation. Using a network-based approach, we investigate how social networks within hemodialysis clinics affect patients' attitudes, contributing to a growing understanding of this dynamic. Our findings emphasize that social networks improve the performance of machine learning models, highlighting the importance of social interactions in clinical settings (Aljurbua et al., 2022). We further introduce Node2VecFuseClassifier, a graph-based model that combines patient interactions with patient characteristics. By comparing problem representations that focus on sociodemographics versus social interactions, we demonstrate that incorporating patient-to-patient and patient-to-staff interactions results in more accurate predictions. This multi-modal analysis, which merges patient experiences with staff expertise, underscores the role of social networks in influencing attitudes toward transplantation (Aljurbua et al., 2024b).
In power systems, we explore the impact of severe weather events that lead to power outages, specifically focusing on predicting weather-induced outages three hours in advance at the county level in the Pacific Northwest of the United States. By utilizing a multi-model multiplex network that integrates data from multiple sources including weather, transmission lines, lightning, vegetation, and social media posts from two leading platforms (Twitter and Reddit), we show how multiplex networks offer valuable insights for predicting power outages. This integration of diverse data sources and network-based modeling emphasizes the importance of leveraging multiple perspectives to enhance the understanding and prediction of power disruptions (Aljurbua et al., 2023). We further present HMN-RTS, a hierarchical multiplex network that classifies disruption severity by temporal learning from integrated weather recordings and social media posts. The multiplex network layers of this framework gather information about power outages, weather, lighting, land cover, transmission lines, and social media comments. By incorporating multiplex network layers consisting of data collected over time and across regions, we demonstrate that HMN-RTS significantly improves the accuracy of predicting the duration of weather-related outages. This framework enables grid operators to make more reliable predictions up to 6 hours in advance, supporting early risk assessment and proactive mitigation (Aljurbua et al., 2024a, 2025a). Additionally, we introduce SMN-WVF, a spatiotemporal multiplex network designed to predict the duration of power outages in distribution grids. By integrating network-based approach and multi-modal data across space and time, SMN-WVF offers a novel method for predicting disruption durations in distribution grids, enhancing decision-making and mitigation efforts while highlighting the critical role of network-based approaches in forecasting (Aljurbua et al., 2025b).
Overall, our research showcases the potential of graph-based models in tackling complex challenges in both power systems and healthcare. By combining the network-based approach with multi-modal data, we present innovative solutions for predicting power outages and understanding patient attitudes.Computer and Information Scienc
Log Linear Models for Prediction and Analysis of Networks
No full textThe heightened research activity in the interdisciplinary field of network science can be attributed to the emergence of the social network computer applications. Researchers understood early on that data describing how entities interconnect is highly valuable and that it offers a deeper understanding about the entities themselves. This is why there were so many studies done about various kinds of networks in the last 10-15 years. The study of the networks from the perspective of computer science usually has two objectives. The first objective is to develop statistical mechanisms capable of accurately describing and modeling observed real-world networks. A good fit of such mechanism suggests the correctness of the model's assumptions and leads to better understanding of the network. A second goal is more practical, a well performing model can be used to predict what will happen to the network in the future. Also, such model can be leveraged to use the information gleaned from network to predict what will happen to the networks entities. One important leitmotif of network research and analysis is wide adaptation of log linear models. In this work we apply this philosophy for study and evaluation of log-linear statistical models in various types of networks. We begin with proposal of the new Temporal Exponential Random Graph Model (tERGM) for the analysis and predictions in the binary temporal social networks. We then extended the model for applications in partially observed networks that change over time. Lastly, we generalize the tERGM model to predict the real-valued weighted links in the temporal non-social networks. The log-linear models are not limited to networks that change over time but can also be applied to networks that are static. One such static network is a social network composed of patients undergoing hemodialysis. Hemodialysis is prescribed to people suffering from the end stage renal disease; the treatment necessitates the attendance, on non-changing schedule, of the hemodialysis clinic for a prolonged time period and this is how the social ties are formed. The new log-linear Social Latent Vectors (SLV) model was applied to study such static social networks. The results obtained from SLV experiments suggest that social relationships formed by patients bear influence on individual patients clinical outcome. The study demonstrates how social network analysis can be applied to better understand the network constituents.Computer and Information Scienc
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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