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1.
《Journal of Informetrics》2014,8(3):683-692
This paper explores a possible approach to a research evaluation, by calculating the renown of authors of scientific papers. The evaluation is based on the citation analysis and its results should be close to a human viewpoint. The PageRank algorithm and its modifications were used for the evaluation of various types of citation networks. Our main research question was whether better evaluation results were based directly on an author network or on a publication network. Other issues concerned, for example, the determination of weights in the author network and the distribution of publication scores among their authors. The citation networks were extracted from the computer science domain in the ISI Web of Science database. The influence of self-citations was also explored. To find the best network for a research evaluation, the outputs of PageRank were compared with lists of prestigious awards in computer science such as the Turing and Codd award, ISI Highly Cited and ACM Fellows. Our experiments proved that the best ranking of authors was obtained by using a publication citation network from which self-citations were eliminated, and by distributing the same proportional parts of the publications’ values to their authors. The ranking can be used as a criterion for the financial support of research teams, for identifying leaders of such teams, etc. 相似文献
2.
基于网络结构挖掘算法的引文网络研究 总被引：1，自引：0，他引：1
本文在对网络结构挖掘的两种典型算法(HITS算法和PageRank算法)进行比较分析的基础上,将PageRank算法应用到大规模引文网络中.对由236 517篇SCI文章构成的引文网络,计算得到每一篇文献的PageRank值,并深入分析了文献的PageRank值与通常使用的引文数指标之间的关系.分析表明:PageRank值具有与引文数很强的相关性和相似的幂律分布特征,但是PageRank算法能够在高引文文献中更好的区别文献的潜在重要性,并在很大程度上削弱作者自引对文献评价客观性的影响. 相似文献
3.
The objective assessment of the prestige of an academic institution is a difficult and hotly debated task. In the last few years, different types of university rankings have been proposed to quantify it, yet the debate on what rankings are exactly measuring is enduring.To address the issue we have measured a quantitative and reliable proxy of the academic reputation of a given institution and compared our findings with well-established impact indicators and academic rankings. Specifically, we study citation patterns among universities in five different Web of Science Subject Categories and use the PageRank algorithm on the five resulting citation networks. The rationale behind our work is that scientific citations are driven by the reputation of the reference so that the PageRank algorithm is expected to yield a rank which reflects the reputation of an academic institution in a specific field. Given the volume of the data analysed, our findings are statistically sound and less prone to bias, than, for instance, ad–hoc surveys often employed by ranking bodies in order to attain similar outcomes. The approach proposed in our paper may contribute to enhance ranking methodologies, by reconciling the qualitative evaluation of academic prestige with its quantitative measurements via publication impact. 相似文献
4.
《Journal of Informetrics》2019,13(2):515-539
Counting of number of papers, of citations and the h-index are the simplest bibliometric indices of the impact of research. We discuss some improvements. First, we replace citations with individual citations, fractionally shared among co-authors, to take into account that different papers and different fields have largely different average number of co-authors and of references. Next, we improve on citation counting applying the PageRank algorithm to citations among papers. Being time-ordered, this reduces to a weighted counting of citation descendants that we call PaperRank. We compute a related AuthorRank applying the PageRank algorithm to citations among authors. These metrics quantify the impact of an author or paper taking into account the impact of those authors that cite it. Finally, we show how self- and circular-citations can be eliminated by defining a closed market of Citation-coins. We apply these metrics to the InSpire database that covers fundamental physics, presenting results for papers, authors, journals, institutes, towns, countries for all-time and in recent time periods. 相似文献
5.
We evaluate author impact indicators and ranking algorithms on two publication databases using large test data sets of well-established researchers. The test data consists of (1) ACM fellowship and (2) various life-time achievement awards. We also evaluate different approaches of dividing credit of papers among co-authors and analyse the impact of self-citations. Furthermore, we evaluate different graph normalisation approaches for when PageRank is computed on author citation graphs.We find that PageRank outperforms citation counts in identifying well-established researchers. This holds true when PageRank is computed on author citation graphs but also when PageRank is computed on paper graphs and paper scores are divided among co-authors. In general, the best results are obtained when co-authors receive an equal share of a paper's score, independent of which impact indicator is used to compute paper scores. The results also show that removing author self-citations improves the results of most ranking metrics. Lastly, we find that it is more important to personalise the PageRank algorithm appropriately on the paper level than deciding whether to include or exclude self-citations. However, on the author level, we find that author graph normalisation is more important than personalisation. 相似文献
6.
We evaluate article-level metrics along two dimensions. Firstly, we analyse metrics’ ranking bias in terms of fields and time. Secondly, we evaluate their performance based on test data that consists of (1) papers that have won high-impact awards and (2) papers that have won prizes for outstanding quality. We consider different citation impact indicators and indirect ranking algorithms in combination with various normalisation approaches (mean-based, percentile-based, co-citation-based, and post hoc rescaling). We execute all experiments on two publication databases which use different field categorisation schemes (author-chosen concept categories and categories based on papers’ semantic information).In terms of bias, we find that citation counts are always less time biased but always more field biased compared to PageRank. Furthermore, rescaling paper scores by a constant number of similarly aged papers reduces time bias more effectively compared to normalising by calendar years. We also find that percentile citation scores are less field and time biased than mean-normalised citation counts.In terms of performance, we find that time-normalised metrics identify high-impact papers better shortly after their publication compared to their non-normalised variants. However, after 7 to 10 years, the non-normalised metrics perform better. A similar trend exists for the set of high-quality papers where these performance cross-over points occur after 5 to 10 years.Lastly, we also find that personalising PageRank with papers’ citation counts reduces time bias but increases field bias. Similarly, using papers’ associated journal impact factors to personalise PageRank increases its field bias. In terms of performance, PageRank should always be personalised with papers’ citation counts and time-rescaled for citation windows smaller than 7 to 10 years. 相似文献
7.
《Journal of Informetrics》2022,16(4):101341
We study an agent-based model for generating citation distributions in complex networks of scientific papers, where a fraction of citations is allotted according to the preferential attachment rule (rich get richer) and the remainder is allocated accidentally (purely at random, uniformly). Previously, we derived and analysed such a process in the context of describing individual authors, but now we apply it to scientific journals in computer and information sciences. Based on the large DBLP dataset as well as the CORE (Computing Research and Education Association of Australasia) journal ranking, we find that the impact of journals is correlated with the degree of accidentality of their citation distribution. Citations to impactful journals tend to be more preferential, while citations to lower-ranked journals are distributed in a more accidental manner. Further, applied fields of research such as artificial intelligence seem to be driven by a stronger preferential component – and hence have a higher degree of inequality – than the more theoretical ones, e.g., mathematics and computation theory. 相似文献
8.
The Web impact of open access social science research 总被引：1，自引：0，他引：1
For a long time, Institute for Scientific Information (ISI) journal citations have been widely used for research performance monitoring of the sciences. For the social sciences, however, the Social Sciences Citation Index® (SSCI®) can sometimes be insufficient. Broader types of publications (e.g., books and non-ISI journals) and informal scholarly indicators may also be needed. This article investigates whether the Web can help to fill this gap. The authors analyzed 1530 citations from Google™ to 492 research articles from 44 open access social science journals. The articles were published in 2001 in the fields of education, psychology, sociology, and economics. About 19% of the Web citations represented formal impact equivalent to journal citations, and 11% were more informal indicators of impact. The average was about 3 formal and 2 informal impact citations per article. Although the proportions of formal and informal online impact were similar in sociology, psychology, and education, economics showed six times more formal impact than informal impact. The results suggest that new types of citation information and informal scholarly indictors could be extracted from the Web for the social sciences. Since these form only a small proportion of the Web citations, however, Web citation counts should first be processed to remove irrelevant citations. This can be a time-consuming process unless automated. 相似文献
9.
Hailey MOONEY 《Learned Publishing》2011,24(2):99-108
It is expected that authors will provide citations for all papers referenced in their writings. The necessity of providing citations for data is not so widely recognized. Proponents of the data‐sharing movement have advocated the citation of datasets in order to recognize contributions and enhance access. This study examines a sample of papers from the Inter‐University Consortium for Political and Social Research (ICPSR) Bibliography of Data‐Related Literature that are based on secondary analysis of datasets available in the ICPSR data archive to determine the data citation practices of authors. The results indicate that many authors fail to cite the data used in secondary analysis studies. Possible reasons for the dismal state of data citation practices are considered, including the recent introduction of data into the scholarly record and its marginalization as an information format. Updating citation practices to include datasets will support data sharing and foster responsible scholarship. 相似文献
10.
11.
Diego Raphael Amancio Osvaldo Novais Oliveira Luciano da Fontoura Costa 《Journal of Informetrics》2012,6(3):427-434
Various factors are believed to govern the selection of references in citation networks, but a precise, quantitative determination of their importance has remained elusive. In this paper, we show that three factors can account for the referencing pattern of citation networks for two topics, namely “graphenes” and “complex networks”, thus allowing one to reproduce the topological features of the networks built with papers being the nodes and the edges established by citations. The most relevant factor was content similarity, while the other two – in-degree (i.e. citation counts) and age of publication – had varying importance depending on the topic studied. This dependence indicates that additional factors could play a role. Indeed, by intuition one should expect the reputation (or visibility) of authors and/or institutions to affect the referencing pattern, and this is only indirectly considered via the in-degree that should correlate with such reputation. Because information on reputation is not readily available, we simulated its effect on artificial citation networks considering two communities with distinct fitness (visibility) parameters. One community was assumed to have twice the fitness value of the other, which amounts to a double probability for a paper being cited. While the h-index for authors in the community with larger fitness evolved with time with slightly higher values than for the control network (no fitness considered), a drastic effect was noted for the community with smaller fitness. 相似文献
12.
通过对2000到2003年期刊论文中的Web引文记录的统计分析，提出了引文有效率、年衰减指数和有效性半衰期等指标。基于这些指标，我们对Web资源是否适合学术引用进行了探讨，并得出结论是：在目前这种互联网环境下，鉴于网络的动态性和不稳定性，Web资源的可查证性是有问题的。同时我们还看到，由于网站发布成本下降，Web信息趋于更加不稳定，其可查证性也随之降低。 相似文献
13.
14.
Feifei Wang Chenran Jia Xiaohan Wang Junwan Liu Shuo Xu Yang Liu Chenyuyan Yang 《Journal of Informetrics》2019,13(3):856-873
Constructing academic networks to explore intellectual structure realize academic community detection, which can promote scientific research innovation and discipline progress, constitutes an important research topic. In this study, tripartite citation is fused with co-citation and coupling relations as a way of weighting the strength of direct citations, and all-author tripartite citation networks were constructed due to the contributions of all authors to the resulting publications. For purpose of exploring the potential of the all-author exclusive and inclusive tripartite citation networks, gene editing is taken as a case study. The extensive experimental comparisons are conducted with the traditional author single-citation networks and first-author tripartite citation network in terms of network structure characteristics, identifying core scholars, and exploring intellectual structures. The following conclusions can be drawn as follows: our all-author tripartite citation networks are able to help identify the most influential scholars in the field of gene editing, and the intellectual structures from exclusive tripartite citation networks are optimal. 相似文献
15.
国外网络引文研究的现状及展望 总被引：1，自引：0，他引：1
国外的网络引文研究可分为四个方面,侧莺点各不相同:Print-Print引文研究着重于网络环境下传统引文数据库的分析;Print-Web引文研究大多集中在网络引文的可获得性方面;Web-Print引文研究注重实证与传统引文的比较;Web-Web引文研究则是将来引文发展的总趋向.网络引文数据的动态件和不可靠性、网络文献缺乏有效控制与规范,以及数据的精确性问题,都给引文分析带来一定困难.目前相关的研究还处于开创性和探索性阶段,未来的网络引文研究将重点从理论体系的构建、研究方法的完善与实践应用的深化三个层面展开. 相似文献
16.
In this study we map out the large-scale structure of citation networks of science journals and follow their evolution in time by using stochastic block models (SBMs). The SBM fitting procedures are principled methods that can be used to find hierarchical grouping of journals that show similar incoming and outgoing citations patterns. These methods work directly on the citation network without the need to construct auxiliary networks based on similarity of nodes. We fit the SBMs to the networks of journals we have constructed from the data set of around 630 million citations and find a variety of different types of groups, such as communities, bridges, sources, and sinks. In addition we use a recent generalization of SBMs to determine how much a manually curated classification of journals into subfields of science is related to the group structure of the journal network and how this relationship changes in time. The SBM method tries to find a network of blocks that is the best high-level representation of the network of journals, and we illustrate how these block networks (at various levels of resolution) can be used as maps of science. 相似文献
17.
This paper is concerned with a framework to compute the importance of webpages by using real browsing behaviors of Web users.
In contrast, many previous approaches like PageRank compute page importance through the use of the hyperlink graph of the
Web. Recently, people have realized that the hyperlink graph is incomplete and inaccurate as a data source for determining
page importance, and proposed using the real behaviors of Web users instead. In this paper, we propose a formal framework
to compute page importance from user behavior data (which covers some previous works as special cases). First, we use a stochastic
process to model the browsing behaviors of Web users. According to the analysis on hundreds of millions of real records of
user behaviors, we justify that the process is actually a continuous-time time-homogeneous Markov process, and its stationary
probability distribution can be used as the measure of page importance. Second, we propose a number of ways to estimate parameters
of the stochastic process from real data, which result in a group of algorithms for page importance computation (all referred
to as BrowseRank). Our experimental results have shown that the proposed algorithms can outperform the baseline methods such
as PageRank and TrustRank in several tasks, demonstrating the advantage of using our proposed framework. 相似文献
18.
《Library & information science research》1998,20(3):251-270
Three methods—explication, physical analysis, and citation patterns—are used to dissect a small literature: the information overload research from library studies. Explication is an exercise in critical reading and the trilevel explication used here examines overt research structure, backward citation chaining, and within- text inquiry. Overt structure seeks standard research characteristics. Backward citation chaining follows the abstract cognitive train of thought and a within-text inquiry analyzes textual anatomy according to implicit cues: syntactic, semantic, and pragmatic. Physical examination considers the text as artifact and inventories physical properties: publishing entity, co-authorship, literature age, or the amount of explanatory materials. Citation patterns follow bibliometric tenets and identify core researchers, co-authorship, linking citations, overall citing behavior, and the degree of peer- and self-citing. Crossdiscipline comparisons arise from a similar analysis of the overload research from consumer science and psychology/psychiatry. Conclusions arise from the literature itself and result in simple evidentiary statements. 相似文献
19.
专利排名算法——运用引用次数与引文网络计算美国专利的研究 总被引：1，自引：0，他引：1
顾立平 《现代图书情报技术》2011,(6):14-19
在网页排名和论文排名基础上,采用引用频次标准和引文网络计算排名数值,建立专利排名算法。分析美国专利和商标局的数据库中的数字图书馆相关专利,研究结果显示专利排名算法能够区分相同引用次数的专利排名。该研究是网页排名算法的一种新型应用。 相似文献
20.
哪些因素会影响学术论文的被引次数是文献计量学领域的一个经典研究议题。目前的研究主要关注论文的内容特征和形式特征与被引次数之间的关系,鲜有研究从文本可读性视角切入这一议题。文本可读性影响读者对文本内容的理解和知识吸收,是一个关乎知识传播效率和研究成果认可度的重要因素。本研究在控制论文知识品质和权威性的基础上,使用文本可读性R值等五个变量研究论文的文本可读性对被引次数的影响。以中文图书情报学知名期刊发表于2016—2020年的论文为研究样本,研究发现论文的文本可读性R值、是否采用复合式标题、是否使用公式和表格对被引次数有显著影响,而是否使用图对被引次数没有显著影响。研究验证了中文情境下文本可读性对论文影响力的实质性作用,研究结果对科研人员改善自身的中文学术写作以及提高研究成果影响力具有重要参考价值。 相似文献