Name Disambiguation from link data in a collaboration graph using temporal and topological features

In a social community, multiple persons may share the same name, phone number or some other identifying attributes. This, along with other phenomena, such as name abbreviation, name misspelling, and human error leads to erroneous aggregation of records of multiple persons under a single reference. Such mistakes affect the performance of document retrieval, web search, database integration, and more importantly, improper attribution of credit (or blame). The task of entity disambiguation partitions the records belonging to multiple persons with the objective that each decomposed partition is composed of records of a unique person. Existing solutions to this task use either biographical attributes, or auxiliary features that are collected from external sources, such as Wikipedia. However, for many scenarios, such auxiliary features are not available, or they are costly to obtain. Besides, the attempt of collecting biographical or external data sustains the risk of privacy violation. In this work, we propose a method for solving entity disambiguation task from link information obtained from a collaboration network. Our method is non-intrusive of privacy as it uses only the time-stamped graph topology of an anonymized network. Experimental results on two real-life academic collaboration networks show that the proposed method has satisfactory performance.Comment: The short version of this paper has been accepted to ASONAM 201

Tracing and Predicting Collaboration for Junior Scholars

Academic publication is a key indicator for measuring scholars' scientific productivity and has a crucial impact on their future career. Previous work has identified the positive association between the number of collaborators and academic productivity, which motivates the problem of tracing and predicting potential collaborators for junior scholars. Nevertheless, the insufficient publication record makes current approaches less effective for junior scholars. In this paper, we present an exploratory study of predicting junior scholars' future co-authorship in three different network density. By combining features based on affiliation, geographic and content information, the proposed model significantly outperforms the baseline methods by 12% in terms of sensitivity. Furthermore, the experiment result shows the association between network density and feature selection strategy. Our study sheds light on the re-evaluation of existing approaches to connect scholars in the emerging worldwide Web of Scholars

Does your surname affect the citability of your publications?

Prior investigations have offered contrasting results on a troubling question: whether the alphabetical ordering of bylines confers citation advantages on those authors whose surnames put them first in the list. The previous studies analyzed the surname effect at publication level, i.e. whether papers with the first author early in the alphabet trigger more citations than papers with a first author late in the alphabet. We adopt instead a different approach, by analyzing the surname effect on citability at the individual level, i.e. whether authors with alphabetically earlier surnames result as being more cited. Examining the question at both the overall and discipline levels, the analysis finds no evidence whatsoever that alphabetically earlier surnames gain advantage. The same lack of evidence occurs for the subpopulation of scientists with very high publication rates, where alphabetical advantage might gain more ground. The field of observation consists of 14,467 scientists in the sciences

The NASA Astrophysics Data System: Data Holdings

Since its inception in 1993, the ADS Abstract Service has become an indispensable research tool for astronomers and astrophysicists worldwide. In those seven years, much effort has been directed toward improving both the quantity and the quality of references in the database. From the original database of approximately 160,000 astronomy abstracts, our dataset has grown almost tenfold to approximately 1.5 million references covering astronomy, astrophysics, planetary sciences, physics, optics, and engineering. We collect and standardize data from approximately 200 journals and present the resulting information in a uniform, coherent manner. With the cooperation of journal publishers worldwide, we have been able to place scans of full journal articles on-line back to the first volumes of many astronomical journals, and we are able to link to current version of articles, abstracts, and datasets for essentially all of the current astronomy literature. The trend toward electronic publishing in the field, the use of electronic submission of abstracts for journal articles and conference proceedings, and the increasingly prominent use of the World Wide Web to disseminate information have enabled the ADS to build a database unparalleled in other disciplines. The ADS can be accessed at http://adswww.harvard.eduComment: 24 pages, 1 figure, 6 tables, 3 appendice

WHY DO RESEARCHERS COLLABORATE WITH INDUSTRY? AN ANALYSIS OF THE WINE SECTOR IN CHILE, SOUTH AFRICA AND ITALY

This paper explores the determinants of the linkages between industry and research organizations – including universities. We present new evidence on three wine producing areas – Piedmont, a region of Italy, Chile, South Africa - that have successfully reacted to the recent structural changes experienced in the industry worldwide. Based on an original data-set, we carry out an econometric exercise to study the microeconomic determinants of researchers' collaborations with industry. The evidence reveals that individual researcher characteristics, such as embeddedness in the academic system, age and sex, matter more than their publishing record or formal degrees.University-Industry Linkages, Innovation System, Wine Sector, Emerging Economies

Attention-Based Deep Learning Model for Predicting Collaborations Between Different Research Affiliations

It is challenging but important to predict the collaborations between different entities which in academia, for example, would enable finding evaluating trends of scientific research collaboration and the provision of decision support for policy formulation and incentive measures. In this paper, we propose an attention-based Long Short-Term Memory Convolutional Neural Network (LSTM-CNN) model to predict the collaborations between different research affiliations, which takes both the influence of research articles and time (year) relationships into consideration. The experimental results show that the proposed model outperforms the competitive Support Vector Machine (SVM), CNN and LSTM methods. It significantly improves the prediction precision by a minimum of 3.23 percent points and up to 10.80 percent points when compared with the mentioned competitive methods, while in terms of the F1-score, the performance is improved by 13.48, 4.85 and 4.24 percent points, respectively.This work was supported in part by the Humanities and Social Science Research Project of the Ministry of Education in China under Grant 17YJCZH262 and Grant 18YJAZH136, in part by the National Natural Science Foundation of China under Grant 61303167, Grant 61702306, Grant 61433012, Grant U1435215, and Grant 71772107, in part by the Natural Science Foundation of Shandong Province under Grant ZR2018BF013 and Grant ZR2017BF015, in part by the Innovative Research Foundation of Qingdao under Grant 18-2-2-41-jch, in part by the Key Project of Industrial Transformation and Upgrading in China under Grant TC170A5SW, and in part by the Scientific Research Foundation of SDUST for Innovative Team under Grant 2015TDJH102

Análisis de influencia de la red de colaboración de opciones reales

Real Options Theory arose as an alternative to valuate flexibilities entrenched in projects and has acquired popularity since the end of the twentieth century. Through bibliometric methods and graph theory, this paper develops an analysis of the collaboration network comprised of Real Options’ researchers, including scientific papers from over the last eighteen years. In this effort, we meticulously identify authors and their co-authorship alliances, finding a distinct topology without a giant component. Developing unweighted and weighted models, the network is unraveled, providing measurement from internationalization propensity and computing different impact metrics, which recognize the most relevant researchers on the subject.La teoría de opciones reales surgió como una alternativa para valorar las flexibilidades arraigadas en proyectos y ha adquirido popularidad desde finales del siglo xx. A través de métodos bibliométricos y teoría de grafos, este documento crea un análisis de la red de colaboración compuesta por los investigadores de opciones reales, que incluye trabajos científicos de dieciocho años. En este esfuerzo identificamos meticulosamente a los autores y sus alianzas de coautoría, encontrando una topología distinta sin un componente gigante. Al desarrollar modelos no ponderados y ponderados, la red se desenreda y proporciona mediciones a partir de la propensión a la internacionalización y el cálculo de diferentes métricas de impacto, que reconocen a los investigadores más relevantes sobre el tema