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1.
The increasingly growing popularity of the collaboration among researchers and the increasing information overload in big scholarly data make it imperative to develop a collaborator recommendation system for researchers to find potential partners. Existing works always study this task as a link prediction problem in a homogeneous network with a single object type (i.e., author) and a single link type (i.e., co-authorship). However, a real-world academic social network often involves several object types, e.g., papers, terms, and venues, as well as multiple relationships among different objects. This paper proposes a RWR-CR (standing for random walk with restart-based collaborator recommendation) algorithm in a heterogeneous bibliographic network towards this problem. First, we construct a heterogeneous network with multiple types of nodes and links with a simplified network structure by removing the citing paper nodes. Then, two importance measures are used to weight edges in the network, which will bias a random walker’s behaviors. Finally, we employ a random walk with restart to retrieve relevant authors and output an ordered recommendation list in terms of ranking scores. Experimental results on DBLP and hep-th datasets demonstrate the effectiveness of our methodology and its promising performance in collaborator prediction. 相似文献
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《Journal of Informetrics》2023,17(3):101422
Several studies have reported on metrics for measuring the influence of scientific topics from different perspectives; however, current ranking methods ignore the reinforcing effect of other academic entities on topic influence. In this paper, we developed an effective topic ranking model, 4EFRRank, by modeling the influence transfer mechanism among all academic entities in a complex academic network using a four-layer network design that incorporates the strengthening effect of multiple entities on topic influence. The PageRank algorithm is utilized to calculate the initial influence of topics, papers, authors, and journals in a homogeneous network, whereas the HITS algorithm is utilized to express the mutual reinforcement between topics, papers, authors, and journals in a heterogeneous network, iteratively calculating the final topic influence value. Based on a specific interdisciplinary domain, social media data, we applied the 4ERRank model to the 19,527 topics included in the criteria. The experimental results demonstrate that the 4ERRank model can successfully synthesize the performance of classic co-word metrics and effectively reflect high citation topics. This study enriches the methodology for assessing topic impact and contributes to the development of future topic-based retrieval and prediction tasks. 相似文献
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《Journal of Informetrics》2022,16(2):101283
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. 相似文献
4.
Gui-Yuan Shi Yi-Xiu Kong Guang-Hui Yuan Rui-Jie Wu An Zeng Matúš Medo 《Journal of Informetrics》2019,13(2):717-725
Identifying the future influential papers among the newly published ones is an important yet challenging issue in bibliometrics. As newly published papers have no or limited citation history, linear extrapolation of their citation counts—which is motivated by the well-known preferential attachment mechanism—is not applicable. We translate the recently introduced notion of discoverers to the citation network setting, and show that there are authors who frequently cite recent papers that become highly-cited in the future; these authors are referred to as discoverers. We develop a method for early identification of highly-cited papers based on the early citations from discoverers. The results show that the identified discoverers have a consistent citing pattern over time, and the early citations from them can be used as a valuable indicator to predict the future citation counts of a paper. The discoverers themselves are potential future outstanding researchers as they receive more citations than average. 相似文献
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科技论文关键词呈现多类型、多关联关系的属性,可以借助具有多层次、多超边的超网络进行表示建模。本研究构建了由研究对象-实验品种-研究用途-技术方法 4层关键词子网和多种关联超边组成的超网络模型,并将该超网络模型用于"农业动物生殖细胞和干细胞调控"领域的科技论文的实证分析。该超网络模型在揭示单层关键词子网同质关联关系的同时,也能挖掘多层子网之间的隐性异质关联关系,从而发现了该领域常用技术方法、实验品种、研究对象和研究用途,同时还发现了该领域的技术空白点和技术应用空白点,这些空白点很可能成为未来的研究热点。 相似文献
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新能源汽车产业已经成为国家战略性产业的一部分,相关研究逐年增多。为此,本文利用CiteSpace III可视化分析软件,将Web of Science中新能源汽车产业的相关文献作为样本数据,通过国家、作者和文献关键词的共现分析,以及引文被引聚类分析等直观可视化的方式展示出国内外新能源汽车产业研究的前沿与热点主题,国家和作者的科研合作情况,揭示国际新能源汽车产业研究的起源文献和核心知识基础,为新能源产业的政策制定、企业的研发形式变化、领域研究选题等方面提供指引。 相似文献
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庞弘燊 《中国科技资源导刊 (中国信息导报)》2017,(1):90-101
在对可视化概念进行概述的基础上,分析目前在知识图谱领域应用的可视化分析方法与软件工具,并对可
应用于多特征项共现的可视化分析方式进行研究,包括社会网络可视化方式以及交叉图技术可视化方式,还对这两种
可用于多特征项共现可视化的具体分析方法、显示方式进行阐述和展示。最后通过对比这两种不同可视化方式的特
点,发现多特征项共现交叉图的可视化技术较好。在应用前景方面,通过应用本文中基于科技文献多特征项共现的图
谱可视化方法和软件工具,可以对研究机构、研究领域、研究学者等发表论文情况进行分析,能够观测所选论文集中
多 相似文献
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[目的/意义]基于科技论文多特征项共现突发强度的分析方法研究是将各学科领域科技论文文献载体中的多特征项共现信息定量化、重点热点突发的信息内容可视化的知识图谱分析方法。从动态论文等文献的文档流中探测出突发的特征项对识别密集的内容、活跃的特征项以及预测文本内容的发展走势具有重要的意义。[方法/过程]本研究针对科技论文多特征项共现的突发监测问题,对比目前已有的突发监测分析算法,将改进后的基于卡方统计的热点词计算方法进一步应用于本研究所设计的多特征项突发共现分析方法,并自主开发多特征项突发共现可视化分析工具,用于科技论文多特征项突发共现的图谱可视化,以期通过该研究来揭示相关科技文献的变化状况及突发的热点内容。[结果/结论]通过将本方法应用到科研机构年度发表论文的监测当中,可以监测分析科研机构发文作者、关键词、发表期刊及其相互间关系(如作者-关键词等)在各年的突发情况,并能通过该分析方法以及交叉图谱进一步解读突发特征项的含义,并能揭示出比分析单一特征项突发情况更为广泛和深入的知识内容。 相似文献
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科研合作是促进科学生产的一种重要形式,探讨不同机构之间的科研论文合著情况,可以有效把握机构合作的整体现状与特征,有助于提高机构合作的绩效。本研究基于2010-2015 年Web of Science 数据库图书情报学领域期刊发表的论文,构建我国图书情报学领域Top15 高产研究机构的合作网络,综合运用文献合著率、合作多样性、合作稳定度、合作绩效等度量指标,分析了机构合作的主要特征及指标间的相互影响。研究发现：我国图书情报学领域的论文数量总体上呈现增长趋势但论文影响力相对有限,香港地区的科研机构在国际上学术认可度领先于大陆地区的科研机构;科研机构间的合作对象不断拓宽、合作密度不断加强、合作论文产出不断提升成为我国近年来图书情报学领域发展的显著特征;国际化的合作团队、多元的合作对象和稳定的合作关系可以为科研机构带来更多的科研成果产出,提高其学术影响力。 相似文献
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《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
11.
[目的/意义] 鉴于异质网络能够揭示数据的多重关系,引入合作网络,构建2-模异质网络,并基于此异质网络,进行作者潜在合作空间的测度与识别,增加作者间合作机会,促进学科知识的交流与融合提供参考。[方法/过程] 以2004-2013年间的图书情报学核心期刊论文为研究对象,定义作者-作者-关键词2模异质网络,挖掘网络中的多重关系;定义潜在合作空间的相关概念及测度公式,并运用共现分析、耦合分析及编译VBA, 程序,对2-模异质网络的作者潜在合作空间进行测度与识别。[结果/结论] 发现图书情报学领域的47位核心潜在合作者;某一研究主题的作者潜在合作空间相同或相近,重叠部分形成了该研究领域的潜在合作团队,这一发现为研究人员寻求潜在合作者提供了便利条件。 相似文献
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[目的/意义]分析和研究环境/生态学科的现状及国际学术合作情况,旨在了解我国在该领域范围内的优势及不足,为我国未来生态环境领域的科研活动以及国家合作方向提供借鉴。[方法/过程]本文以2009—2019年WOS核心数据库中的5640篇环境/生态学科高被引论文为数据源,对时空分布与影响力进行计量分析,同时运用复杂网络分析法,构建国际合作网络结构,探析合作的现状和特点。[结果/结论]结果显示:环境/生态学高被引论文的国际合作研究呈现积极上升态势,各国间知识流动日益频繁,但国家间合作分布异质性明显。中国在该学科高被引论文发表数以绝对优势占居领先地位,但论文国际合作比例偏低,未来需要加强论文的原始创新,提高研究成果的国际影响力。 相似文献
14.
主题词组合新颖性与论文学术影响力的关系研究 总被引：1，自引：0，他引：1
[目的/意义] 研究学术论文内容的组合新颖性与其学术影响力的关系,为研究论文的学术影响力提供新的研究视角。[方法/过程] 采用文本挖掘方法对论文题目、摘要和关键词中的主题词进行提取,通过构建领域主题词共现网络,为每篇论文设计了新颖组合率、中等组合率和常规组合率3个指标,将领域论文划分为不同的新颖性/常规性类型,然后对不同类型论文中高被引论文所占的比例进行统计分析。[结果/结论] 同时具有主题词组合高新颖性和高常规性特点的论文成为高被引论文的几率显著高于其他类型的论文,因此建议研究者在科学研究中应注重新颖知识与常规知识的适当组合。 相似文献
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[目的/意义]运用深度学习技术,提出结合时间和空间特征的测度(速度、覆盖度和迂回度)方法,用于量化学者研究主题演化,从而为基于内容的学者评价提供量化依据。[方法/过程]提出三维指标框架,其中速度反映作者改变研究主题快慢的平均程度,覆盖度反映作者研究内容所覆盖的主题广度,迂回度反映作者研究路径的曲折性。使用微软学术数据集中计算机科学的作者进行实证研究,并考察学者研究主题演化的三维测度和学者学术影响力和生产力的关系。[结果/结论] 实证研究结果显示,覆盖度与总被引量和总发文量的关系为单调递减,这一特征说明聚焦于特定研究主题较为深入的作者,其发文量和影响力都较大。作者研究主题演化的"速度"和"迂回度"与总被引量、总发文量都存在先增加后减少的倒U型关系。所提出的多维度指标框架不仅可在理论上丰富科学计量学对于学者研究主题转移演化及其机制的理解,而且结合深度学习模型提出了问题的解决思路。 相似文献
17.
Christos Giatsidis Giannis Nikolentzos Chenhui Zhang Jie Tang Michalis Vazirgiannis 《Journal of Informetrics》2019,13(2):757-768
Traditionally, citation count has served as the main evaluation measure for a paper's importance and influence. In turn, many evaluations of authors, institutions and journals are based on aggregations upon papers (e.g. h-index). In this work, we explore measures defined on the citation graph that offer a more intuitive insight into the impact of a paper than the superficial count of citations. Our main argument is focused on the identification of influence as an expression of the citation density in the subgraph of citations built for each paper. We propose two measures that capitalize on the notion of density providing researchers alternative evaluations of their work. While the general idea of impact for a paper can be viewed as how many researchers have shown interest to a piece of work, the proposed measures are based on the hypothesis that a piece of work may have influenced some papers even if they do not contain references to that piece of work. The proposed measures are also extended to researchers and journals. 相似文献
18.
[目的/意义]在引文分析中,可通过论文的一些属性特征对其未来的被引情况进行预测,并通过预测结果对论文、论文作者、作者所属机构及出版物做出评价。[方法/过程] 从出版物、作者和论文三个方面对影响论文被引的多个因素展开研究,以图书馆学情报学领域被SCI索引的论文作为分析及验证数据,使用逻辑回归、GBDT、XGBoost、AdaBoost、随机森林等算法进行预测,使用多组评测指标对比不同预测方法的效果,并使用GBDT识别对论文被引影响较大的因素。[结果/结论]确定三个方面的影响因素对论文被引预测的影响程度,构建预测模型,并较好地预测论文在未来一段时间的被引情况。大量实验分析发现GBDT、XGBoost和随机森林的预测能力较强,且预测的时间段越长,效果也就相对越好。 相似文献
19.
为深入把握研究前沿,并了解其中高被引论文的特征,以ESI 数据库中(数据截至2017 年6 月底) 全球最受关注的前10 位(TOP 10) 研究前沿中的高被引论文(共计486 篇) 为样本,利用Excel 2010 对其涉及学科、来源期刊特征、作者地域分布3 个方面进行了统计分析,希望能为国内期刊在追踪前沿学术论文,以及提升论文影响力方面提供参考. 相似文献
20.
张海超 《中国科技资源导刊》2016,(6):68-79
本文提出一种基于期刊论文的国家研究实力多维评价方法,从产出与影响力维度、国际和地区合作维度、研究机构维度、作者维度以及研究领域维度等来评价国家研究实力.最后以Nature期刊为例,对比分析中国大陆和美国的研究实力,揭示中美学术研究现状,找出我国的不足并提出应对策略,为我国科研创新提出建议. 相似文献