Ranking scientific articles based on bibliometric networks with a weighting scheme     

Yu Zhang  Min Wang  Florian Gottwalt  Morteza Saberi  Elizabeth Chang 《Journal of Informetrics》2019,13(2):616-634

As the volume of scientific articles has grown rapidly over the last decades, evaluating their impact becomes critical for tracing valuable and significant research output. Many studies have proposed various ranking methods to estimate the prestige of academic papers using bibliometric methods. However, the weight of the links in bibliometric networks has been rarely considered for article ranking in existing literature. Such incomplete investigation in bibliometric methods could lead to biased ranking results. Therefore, a novel scientific article ranking algorithm, W-Rank, is introduced in this study proposing a weighting scheme. The scheme assigns weight to the links of citation network and authorship network by measuring citation relevance and author contribution. Combining the weighted bibliometric networks and a propagation algorithm, W-Rank is able to obtain article ranking results that are more reasonable than existing PageRank-based methods. Experiments are conducted on both arXiv hep-th and Microsoft Academic Graph datasets to verify the W-Rank and compare it with three renowned article ranking algorithms. Experimental results prove that the proposed weighting scheme assists the W-Rank in obtaining ranking results of higher accuracy and, in certain perspectives, outperforming the other algorithms.  相似文献   

An empirical approach based on quantile regression for estimating citation ageing     

《Journal of Informetrics》2019,13(2):738-750

An aspect of citation behavior, which has received longstanding attention in research, is how articles’ received citations evolve as time passes since their publication (i.e., citation ageing). Citation ageing has been studied mainly by the formulation and fit of mathematical models of diverse complexity. Commonly, these models restrict the shape of citation ageing functions and explicitly take into account factors known to influence citation ageing. An alternative—and less studied—approach is to estimate citation ageing functions using data-driven strategies. However, research following the latter approach has not been consistent in taking into account those factors known to influence citation ageing. In this article, we propose a model-free approach for estimating citation ageing functions which combines quantile regression with a non-parametric specification able to capture citation inflation. The proposed strategy allows taking into account field of research effects, impact level effects, citation inflation effects and skewness in the distribution of cites effects. To test our methodology, we collected a large dataset consisting of more than five million citations to 59,707 research articles spanning 12 dissimilar fields of research and, with this data in hand, tested the proposed strategy.  相似文献   

Can Google Scholar and Mendeley help to assess the scholarly impacts of dissertations?     

Kayvan Kousha  Mike Thelwall 《Journal of Informetrics》2019,13(2):467-484

Dissertations can be the single most important scholarly outputs of junior researchers. Whilst sets of journal articles are often evaluated with the help of citation counts from the Web of Science or Scopus, these do not index dissertations and so their impact is hard to assess. In response, this article introduces a new multistage method to extract Google Scholar citation counts for large collections of dissertations from repositories indexed by Google. The method was used to extract Google Scholar citation counts for 77,884 American doctoral dissertations from 2013 to 2017 via ProQuest, with a precision of over 95%. Some ProQuest dissertations that were dual indexed with other repositories could not be retrieved with ProQuest-specific searches but could be found with Google Scholar searches of the other repositories. The Google Scholar citation counts were then compared with Mendeley reader counts, a known source of scholarly-like impact data. A fifth of the dissertations had at least one citation recorded in Google Scholar and slightly fewer had at least one Mendeley reader. Based on numerical comparisons, the Mendeley reader counts seem to be more useful for impact assessment purposes for dissertations that are less than two years old, whilst Google Scholar citations are more useful for older dissertations, especially in social sciences, arts and humanities. Google Scholar citation counts may reflect a more scholarly type of impact than that of Mendeley reader counts because dissertations attract a substantial minority of their citations from other dissertations. In summary, the new method now makes it possible for research funders, institutions and others to systematically evaluate the impact of dissertations, although additional Google Scholar queries for other online repositories are needed to ensure comprehensive coverage.  相似文献   

Predicting citation counts based on deep neural network learning techniques     

《Journal of Informetrics》2019,13(2):485-499

With the growing number of published scientific papers world-wide, the need to evaluation and quality assessment methods for research papers is increasing. Scientific fields such as scientometrics, informetrics, and bibliometrics establish quantified analysis methods and measurements for evaluating scientific papers. In this area, an important problem is to predict the future influence of a published paper. Particularly, early discrimination between influential papers and insignificant papers may find important applications. In this regard, one of the most important metrics is the number of citations to the paper, since this metric is widely utilized in the evaluation of scientific publications and moreover, it serves as the basis for many other metrics such as h-index. In this paper, we propose a novel method for predicting long-term citations of a paper based on the number of its citations in the first few years after publication. In order to train a citation count prediction model, we employed artificial neural network which is a powerful machine learning tool with recently growing applications in many domains including image and text processing. The empirical experiments show that our proposed method outperforms state-of-the-art methods with respect to the prediction accuracy in both yearly and total prediction of the number of citations.  相似文献   

The distinction machine: Physics journals from the perspective of the Kolmogorov–Smirnov statistic     

《Journal of Informetrics》2019,13(4):100982

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中华武术与网络游戏     

柏建晴 《搏击·武术科学》2011,8(4):8-9

武术渗透到网络游戏领域,打造出功夫网游这样一种虚幻艺术形式,推动了武术产业的发展。武术与网游的结合把现代互联网技术和传统中国文化巧妙地结合在一起,通过互联网互动的手段来宣传中国传统文化,提高青少年对武术的认知,我们更提倡青少年在通过玩游戏提高文化传承度的同时,又可以强身健体。  相似文献   

略论技击的构成关系——以冷兵器时代军事需求为例     

刘宇峰 《搏击·武术科学》2011,8(7):27-28

技击概念是了解技击本身和建立技击与现代武术关系首先要解决的问题。明确技击的构成关系,不仅为进一步研究技击演变提供理论支撑,更将为明确现代技击概念奠定基础。文章采用文献资料法和逻辑推理的研究方法进行研究。阐明冷兵器时代军事需求下,技击是由技与击构成的有机体,技为击做必要的准备,击在杀敌的过程中验证技的实效性;技与击在形成于应用的过程中互相作用：形成技为击做准备,击验证技的实效性并更加完善技的循环作用关系。  相似文献   

《涉外民事关系法律适用法》颁布前后我国国际私法立法状况浅析     

徐丽 《济宁师范专科学校学报》2011,(4):81-84

改革开放以来,中国国际私法的立法有了长足发展。然而现有的规定不管在立法形式上还是内容上都存在明显的不足,很难应付涉外民商事关系的日益复杂化。随着《中华人民共和国涉外民事关系法律适用法》的出台,相较于之前我国的国际私法立法,有了质的飞跃,其立法的价值取向也迎合了当代国际私法的发展趋势,如何研究和完善《涉外民事关系法律适用法》便显得尤为重要。  相似文献   

基于工作过程导向的高职《公共关系实务》课程改革与实践     

刘惠苑 《安徽职业技术学院学报》2011,10(4):74-76

"工作过程导向"是当前我国职业教育课程开发的精髓,课程内容设计以"工作过程"为基点编排。文章在论述高职《公共关系实务》理论研究与实践教学的基础上,探索基于工作导向的课程设计模式,归纳为"工作任务分解"、"确定过程指导式教材"等,指出课程设计的关键点在于职业能力分解。  相似文献   

专利权人引用网络中的媒介角色研究     

肖宇锋  江洪  董克 《现代图书情报技术》2011,(11):60-66

对专利权人引用网络中的3种类型的媒介角色的概念、算法等进行梳理,根据媒介角色理论和知识交流理论对媒介角色是否影响专利权人技术创新能力做理论推导,并提出3个假设。通过实证分析发现,不同的角色影响程度各不相同:中介中心性对专利权人技术创新能力的影响力最大;5种桥系数小于中介中心性,影响最小的是结构洞约束。  相似文献