Knowledge spillovers: Does the geographic proximity effect decay over time? A discipline-level analysis,accounting for cognitive proximity,with and without self-citations

This work analyzes the variation over time of the effect of geographic distance on knowledge flows. The flows are measured through the citations exchanged between scientific publications, including and excluding self-citations. To calculate geographic distances between citing and cited publication, each publication is associated with a “prevailing” territory, according to the authors’ affiliations. We then apply a gravity model to account for the research size of the territories, in terms of cognitive proximity of citing-cited publications. The field of observation is the 2010–2017 world publications citing the 2010–2012 Italian publications, as indexed in the Web of Science. The results show that in domestic knowledge flows, geographic proximity remains an influential factor through time, although with differences among disciplines and trends of attenuating effects. Finally, we replicate the analyses of knowledge flows but with the exclusion of self-citations: in this manner the effect of geographic proximity seems reduced, particularly at the national scale, but the differences (with vs without self-citations) lessen through time. As shown in previous works, the effect of distance on continental flows is modest (imperceptible for intercontinental flows), yet here too time has some influence, including concerning exclusion of self-citations.     

A rational indicator of scientific creativity

A model is proposed for the creation and transmission of scientific knowledge, based on the network of citations among research articles. The model allows to assign to each article a non-negative value for its creativity, i.e. its creation of new knowledge. If the entire publication network is truncated to the first neighbors of an article (the n references that it makes and the m citations that it receives), its creativity value becomes a simple function of n and m. After splitting the creativity of each article among its authors, the cumulative creativity of an author is then proposed as an indicator of her or his merit of research. In contrast with other merit indicators, this creativity index yields similar values for the top scientists in two very different areas (life sciences and physics), thus offering good promise for interdisciplinary analyses.     

Citation bias in measuring knowledge flow: Evidence from the web of science at the discipline level

Knowledge flow between scientific disciplines has commonly been measured based on citation data. Previous studies using citing relationships have mostly considered direct citations but have paid little attention to indirect citations (IDC) to indicate how knowledge diffusion from one discipline to another via one or more intermediaries. In this study, we measured knowledge flow between disciplines from two perspectives: direct citations (DC) and discipline potential energy (DPE), which is proposed to combine both direct and indirect citations. Data were collected from the Web of Science (WoS) database. Findings include: (1) DPE overshadows previous measures by considering not only direct citations but also indirect citations between disciplines which was usually ignored in previous measures, and revealed that the knowledge contribution of some disciplines had been underestimated by previous measures, such as Physics and Engineering. (2) The proportion of IDC contribution is close to that of direct knowledge contribution when the discipline scale is removed, which suggests that it is essential to consider IDC to distinguish the knowledge relationship (net-outflow/inflow) between disciplines. (3) Both measurements show that Biology & Biochemistry has always been the top discipline with the highest net outflow of knowledge, which is inconsistent with the history of science that Mathematics, Physics and Chemistry would be the highest net outflow disciplines. The results show that even considering IDC does not fully reveal the knowledge contribution and academic influence of disciplines. This paper also analyzes the potential reasons for citation bias in revealing the contribution of disciplinary knowledge from a citation perspective. Therefore, caution should be taken in the use of citations as a primary measure of knowledge flow.     

Can epidemic models describe the diffusion of topics across disciplines?

This paper introduces a new approach to describe the spread of research topics across disciplines using epidemic models. The approach is based on applying individual-based models from mathematical epidemiology to the diffusion of a research topic over a contact network that represents knowledge flows over the map of science—as obtained from citations between ISI Subject Categories. Using research publications on the protein class kinesin as a case study, we report a better fit between model and empirical data when using the citation-based contact network. Incubation periods on the order of 4–15.5 years support the view that, whilst research topics may grow very quickly, they face difficulties to overcome disciplinary boundaries.     

数字环境下科技期刊的引领与服务作用

以学术期刊的基本功能作为切入点,通过大数据分析方法验证科技期刊相对其他的论文载体在科学研究成果出版和传播方面的引领作用;同时,探讨在数字出版和传播环境下,运用便捷的全流程出版平台和新型数字出版模式,不断提升学术期刊对于作者和读者的服务作用。希望从一个新的视角为广大科技期刊提供启发和思路。     

Poincare: Recommending Publication Venues via Treatment Effect Estimation

Choosing a publication venue for an academic paper is a crucial step in the research process. However, in many cases, decisions are based solely on the experience of researchers, which often leads to suboptimal results. Although there exist venue recommender systems for academic papers, they recommend venues where the paper is expected to be published. In this study, we aim to recommend publication venues from a different perspective. We estimate the number of citations a paper will receive if the paper is published in each venue and recommend the venue where the paper has the most potential impact. However, there are two challenges to this task. First, a paper is published in only one venue, and thus, we cannot observe the number of citations the paper would receive if the paper were published in another venue. Secondly, the contents of a paper and the publication venue are not statistically independent; that is, there exist selection biases in choosing publication venues. In this paper, we formulate the venue recommendation problem as a treatment effect estimation problem. We use a bias correction method to estimate the potential impact of choosing a publication venue effectively and to recommend venues based on the potential impact of papers in each venue. We highlight the effectiveness of our method using paper data from computer science conferences.     

How to detect the sleeping beauty papers and princes in technology considering indirect citations?

In science-technology research, papers and patents are used to represent science and technology, respectively. Detecting sleeping beauty papers and their princes in technology (patent field) could uncover dynamic knowledge contributions from science (paper field) to technology (patent field). However, previous studies have mainly focused on sleeping beauty in science. Some studies have examined SB patents in technology, but SB papers in patents are rarely studied and need to be further discussed. In addition, knowledge could flow along citations. Thus, if one paper is cited by one patent's reference (indirect citation), it also contributes to the patent, even though the patent does not directly cite it. At the same time, indirect citations are rarely discussed in sleeping beauty studies. This could lead to a loss of significant information. Therefore, to reveal the dynamic knowledge contribution from science to technology considering indirect citations, this study proposed a new method of mining sleeping beauty papers in technology and their princes. The lithium-ion battery domain is selected as a case study. The findings are as follows: (1) Most papers do not contribute knowledge to technology continuously, even when considering indirect citations, and the time-varying knowledge contribution strength changes significantly overtime. (2) The knowledge contribution strength with a time delay of more than 11 years occupies 80% of the total knowledge contribution strength. It is suggested that the window period of paper publication evaluation be extended. (3) 22 sleeping beauty papers in technology are detected. Nine papers are among the top 10 regarding the total knowledge contribution strength. (4) The princes of 9 typical sleeping beauty papers in technology are all papers. This implies that the awakening of these papers in technology was all provoked by scientific development.     

Effect of publication month on citation impact

A standard procedure in citation analysis is that all papers published in one year are assessed at the same later point in time, implicitly treating all publications as if they were published at the exact same date. This leads to systematic bias in favor of early-months publications and against late-months publications. This contribution analyses the size of this distortion on a large body of publications from all disciplines over citation windows of up to 15 years. It is found that early-month publications enjoy a substantial citation advantage, which arises from citations received in the first three years after publication. While the advantage is stronger for author self-citations as opposed to citations from others, it cannot be eliminated by excluding self-citations. The bias decreases only slowly over longer citation windows due to the continuing influence of the earlier years’ citations. Because of the substantial extent and long persistence of the distortions, it would be useful to remove or control for this bias in research and evaluation studies which use citation data. It is demonstrated that this can be achieved by using the newly introduced concept of month-based citation windows.     

Main path analysis considering citation structure and content: Case studies in different domains

Main path analysis (MPA) is an effective method widely accepted in science and technology for extracting knowledge diffusion paths. Traditional citation analysis assumes that all citations are treated equally. In contrast, this paper proposes a new MPA framework from the perspective of citation structure and content. Three indicators are considered to adjust edge weight: (1) Structural similarity, (2) Topic similarity and (3) Sentiment analysis. This study takes the bullwhip effect and the Internet of Things domain as examples to verify the reliability and feasibility of improved MPA. The results show that the improved main path uncovers the knowledge trajectories appropriately, which has an ability to distinguish citations and detect important papers. This research enriches MPA theory and provides future research directions from perspective of citation structure and content.     

The role of geographical proximity in knowledge diffusion,measured by citations to scientific literature

This paper analyses the influence of geographic distance on knowledge flows, measured through citations to scientific publications. Previous works using the same approach are limited to single disciplines. In this study, we analyse the Italian scientific production in all disciplines matured in the period 2010–2012. To calculate the geographic distances between citing and cited publications, each one is associated with a “prevalent” territory on the basis of the authors’ affiliations. The results of the application of a gravity model, estimated using ordinary least squares regression, show that despite the spread of IT, geographic distance continues to be an influential factor in the process of knowledge flows between territories. In particular, the analysis reveals that the effect of geographic distance on knowledge flows is significant at the national level, not negligible at the continental level, but completely irrelevant at the intercontinental level.     

科学文献网络中的引文扩散——以2011年诺贝尔化学奖获奖论文为例

科学知识借助引用关系发生动态扩散,客观记录科学发展与演化的轨迹。由于知识之间存在千丝万缕的联系,以孤立的观点看待科学知识的影响与价值往往得到的是片面的感观。本文从联系的视角观察科学知识产出,尝试通过被引、引用、文献耦合与共被引等文献关系为单篇论著构建引文扩散网络,探讨"文献嵌入网络"的概念、测度方式及其在引文扩散过程中起到的特殊作用。案例分析显示,科学知识的形成相伴于科学知识网络的发展,同时也受到这个网络的影响:科学知识向科学领域的扩散,遵循从核心领域向周边领域的扩散模式;施引文献可以揭示目标文献中没有显式呈现的信息;四种文献关系之间可能存在相当程度的重合,引文扩散过程显示出知识的"黏滞性"与非常明显的"小世界"特征。对科学产出的扩散网络进行量化有助于为全面评价其价值提供更多客观依据。     

Exploring the “awakening effect” in knowledge diffusion: a case study of publications in the library and information science domain

Inspired by “sleeping beauties in science”, we proposed that the awakening effect in knowledge diffusion is ubiquitous, whereas the “prince” paper has the strongest effect. To test this hypothesis, a three-layer super-network model depicting the knowledge diffusion trajectory is designed and the diffusion path of the awakening effect (defined on the basis of influential strength) is simulated. In detail, the model is built based on the citation network and collaboration network of 63785 publications in the library and information science domain. Through meta-paths in this super-network, the influential strength of a paper and the awakening effect from neighboring papers can be quantified into 36 numerical features. By testing the effectiveness of these features in citation counts prediction, we try to prove our hypothesis. Thus an effective predictor in machine learning is trained upon these features. Using this predictor, we showed that most neighboring papers in the super-network had effects on future citation counts. The effectiveness of these features is again demonstrated through experiments on papers with different publication years. We also did a case study on papers that were significantly affected by the awakening effect, and found that the model proposed in this paper can also be used to explain some common phenomena in knowledge diffusion. All results show that the awakening effect could be not only ubiquitous but also quantifiable.     

A citation index with allowance for the implicit diffusion of scientific knowledge

This paper suggests a new scientometric index that estimates knowledge diffusion and has two constituents: the first one is equivalent to a usual citation index, i.e., it describes the visible diffusion of scientific knowledge; the second one reflects the implicit diffusion of scientific knowledge and is expressed through the number of implicit citations. The practical value of the suggested index is that it permits implicit initiators of the scientific mainstream to be easily identified. The distinctive feature of such scientists is the large value of the suggested citation index and the low value of the usual citation index.     

科学知识网络的形成与演化(Ⅰ):共词网络方法的提出

知识网络观已经被情报学界普遍接受.已往的科学知识网络研究多以引文关系和合著关系为线索,其结果揭示的是科学界高阶的学科结构和智力结构,而非知识概念结构.为了在微观层面上揭示科学知识网络的形成与演化机理,我们将目光关注在了文章关键词与关键词共现关系上.通过回顾认知地图、科学知识领域绘图与共词分析三个领域的发展历史,本文提出了共词网络方法,并以此为基础分析了科学知识体系的网络结构和知识单元的增长模式.借助对共词网络的结构与演化过程的分析,本文首次从词汇维度定性解释了科学知识网络的形成与演化机理.     

我国科研机构合作网络知识扩散研究——以“生物多样性”研究为例

通过对“生物多样性”研究高产机构所构成的合作网络进行社会网络分析,揭示科研机构合作网络知识扩散规律,剖析我国当前科研合作网络知识交流、扩散的特点。认为科研合作网络已成为科研领域的常态化事物,科研合作网络的结构对知识扩散具有重要要影响,因此,有必要全面探索其自身发展演变的规律,以促进科学知识的交流与扩散,同时也为我国科研政策的发展提供支持。     

Author-team diversity and the impact of scientific publications: Evidence from physics research at a national science lab

In the second half of the 20th century, scientific research in physics, chemistry, and engineering began to focus on the use of large government-funded laboratories. This shift toward so-called big science also brought about a concomitant change in scientific work itself, with a sustained trend toward the use of highly specialized scientific teams, elevating the role of team characteristics on scientific outputs. The actual impact of scientific knowledge is commonly measured by how often peer-reviewed publications are, in turn, cited by other researchers. Therefore, how characteristics such as author team seniority, affiliation diversity, and size affect the overall impact of team publications was examined. Citation information and author demographics were reviewed for 123 articles published in Physical Review Letters from 2004 to 2006 and written by 476 scientists who used the National High Magnetic Field Laboratory's facilities. Correlation analysis indicated that author teams that were more multi-institutional and had homogeneous seniority tended to have more senior scientists. In addition, the analysis suggests that more mixed seniority author teams were likely to be less institutionally dispersed. Quantile regression was used to examine the relationships between author-team characteristics and publication impact. The analysis indicated that both weighted average seniority and average seniority had a negative relationship with the number of citations the publication received. Furthermore, the analysis also showed a positive relationship between first-author seniority and the number of citations, and a negative relationship between the number of authors and the number of citations.     

Revisiting the scaling of citations for research assessment

Over the past decade, national research evaluation exercises, traditionally conducted using the peer review method, have begun opening to bibliometric indicators. The citations received by a publication are assumed as proxy for its quality, but they require standardization prior to use in comparative evaluation of organizations or individual scientists: the citation data must be standardized, due to the varying citation behavior across research fields. The objective of this paper is to compare the effectiveness of the different methods of normalizing citations, in order to provide useful indications to research assessment practitioners. Simulating a typical national research assessment exercise, he analysis is conducted for all subject categories in the hard sciences and is based on the Thomson Reuters Science Citation Index-Expanded^®. Comparisons show that the citations average is the most effective scaling parameter, when the average is based only on the publications actually cited.     

On the time evolution of received citations,in different scientific fields: An empirical study

The time evolution of mean received citations is calculated on a sample of journals from two ISI subject categories (“Chemistry, multidisciplinary”, ISI Science Edition, and “Management”, ISI Social Science edition) with the use of an original methodology. Mean received citations are plotted against the time gap in years existing between publication of the cited article and received citations. For most Chemistry journals in the sample the maximum number of average received citations occurs two years after publication, and then a decrease is experimented. Some peculiar cases present a different trend. Management journals, conversely, do not present in most cases a peak of citations: average received citations instead grow from year of publication to the age of 10 years (maximum time gap studied). A subsample of journals show similar results for longer time series (up to 23 years). Medians of average received citations per year partly show a similar behavior. Results suggest that citedness follows very different trends in very different fields, and partly suggest why differences in Journal Impact Factor exist between different categories. At the end of the work conclusions are drawn, together with suggestions for future research.     

Predicting citation counts based on deep neural network learning techniques

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.     

基于共被引网络群体中心度的我国人文社会科学领域知识扩散网络研究

研究我国人文社会科学各学科间知识扩散网络和路径,可以为开展跨学科研究和促进学科之间的交叉融合发展提供参考。采用群体中心度指标,基于文献的引证关系和共被引网络对不同学科的网络位置进行了测度,探析了以共被引网络群体中心度为测量指标的我国人文社会科学领域知识交叉与知识扩散状况。结果表明经济学、政治学、法学等学科作为我国人文社会科学领域的重点研究领域,在整个科学知识网络中扮演着重要的角色,其研究内容本身具有较强的学科交叉性,且对各学科都有较强的辐射作用。此外,马克思主义学科对经济学、心理学对教育学和体育学、哲学对政治学、环境科学对人文地理学等也有较强的知识扩散能力。