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1.
《Journal of Informetrics》2023,17(1):101374
Academic collaboration prediction is considered to be an important way to help scholars expand their research horizons and explore a vast and suitable range of partners. However, existing studies mainly rely on historical collaborations for future predictions, which has limitations in digging into credible collaboration possibilities in a wide range of cross-disciplinary contexts. In view of this, this study tries to combine three typical citation relationships (including direct citation, co-citation, and coupling) to predict prospective collaborations based on citation information that reflects the characteristics of scholars’ knowledge structure and research habits, which is supposed to provide supplement and extension for traditional implementation. To this end, we construct all-author tripartite citation networks based on the bibliographic data in the field of gene editing, and apply the Node2vec and Multi-node2vec algorithms to predict collaborations between authors in both single and multiple layers. According to compare with that of link prediction indicators (including CN, AA, PA and RA, etc.) commonly used for traditional collaboration networks, it is found that the prediction results in the multilayer all-author tripartite citation network should be relatively more accurate. The results will be helpful for scholars in the field of gene editing to explore potential collaborators with an implicit research connection. 相似文献
2.
探究我国档案学学术群体共被引网络,可以为优化我国档案学学术群体的内部结构提供一定指导,从而促进知识交流和学科发展。本文以CSSCI数据库中《档案学通讯》和《档案学研究》1997—2016年的引文数据为数据源,采用社会网络分析方法构建档案学学术群体的共被引网络,并分析网络密度、中心度、凝聚子群等网络特性,得出我国档案学学术群体已具备一定规模、共被引网络连通性好、呈现出明显的核心—边缘分布等结论,并提出充分实现高影响力学者的关键作用和价值、加强次级团体与其他学者的联系、重视档案实践工作者在学术研究中的重要作用以及推进我国档案学研究方向的深化和细化等发展建议。 相似文献
3.
《Journal of Informetrics》2022,16(3):101310
As science is becoming more interdisciplinary and potentially more data driven over time, it is important to investigate the changing specialty structures and the emerging intellectual patterns of research fields and domains. By employing a clustering-based network approach, we map the contours of a novel interdisciplinary domain – research using social media data – and analyze how the specialty structures and intellectual contributions are organized and evolve. We construct and validate a large-scale (N = 12,732) dataset of research papers using social media data from the Web of Science (WoS) database, complementing it with citation relationships from the Microsoft Academic Graph (MAG) database. We conduct cluster analyses in three types of citation-based empirical networks and compare the observed features with those generated by null network models. Overall, we find three core thematic research subfields – interdisciplinary socio-cultural sciences, health sciences, and geo-informatics – that designate the main epicenter of research interests recognized by this domain itself. Nevertheless, at the global topological level of all networks, we observe an increasingly interdisciplinary trend over the years, fueled by publications not only from core fields such as communication and computer science, but also from a wide variety of fields in the social sciences, natural sciences, and technology. Our results characterize the specialty structures of this domain at a time of growing emphasis on big social data, and we discuss the implications for indicating interdisciplinarity. 相似文献
4.
学术社交网络用户行为研究进展 总被引：1，自引：1，他引：0
[目的/意义]开展学术社交网络用户行为研究文献的引文分析,以了解该领域研究概貌,并归纳识别学术社交网络用户行为的主要研究方向及进展,为后续研究提出建议。[方法/过程]系统搜集学术社交网络用户行为研究文献,整理形成文献集,运用引文分析方法,统计出高频关键词、高被引文献和高被引作者,并进行定性内容分析,概述学术社交网络用户行为主要研究方向及进展,提出研究结论和建议。[结果/结论]学术社交网络用户行为研究主要集中在学术社交网络的采用情况、社会网络结构、使用行为模式、使用行为影响因素以及使用障碍5个方面,并在各方面取得了一定的研究进展,但存在研究不充分、理论支撑不足等问题。 相似文献
5.
《Journal of Informetrics》2022,16(1):101235
With the advancement of science and technology, the number of academic papers published each year has increased almost exponentially. While a large number of research papers highlight the prosperity of science and technology, they also give rise to some problems. As we know, academic papers are the most intuitive embodiment of the research results of scholars, which can reflect the level of researchers. It is also the standard for evaluation and decision-making of them, such as promotion and allocation of funds. Therefore, how to measure the quality of an academic paper is very critical. The most common standard for measuring the quality of academic papers is the number of citation counts of them, as this indicator is widely used in the evaluation of scientific publications. It also serves as the basis for many other indicators (such as the h-index). Therefore, it is very important to be able to accurately predict the citation counts of academic papers. To improve the effective of citation counts prediction, we try to solve the citation counts prediction problem from the perspective of information cascade prediction and take advantage of deep learning techniques. Thus, we propose an end-to-end deep learning framework (DeepCCP), consisting of graph structure representation and recurrent neural network modules. DeepCCP directly uses the citation network formed in the early stage of the paper as the input, and outputs the citation counts of the corresponding paper after a period of time. It only exploits the structure and temporal information of the citation network, and does not require other additional information. According to experiments on two real academic citation datasets, DeepCCP is shown superior to the state-of-the-art methods in terms of the accuracy of citation count prediction. 相似文献
6.
《Journal of Informetrics》2022,16(1):101225
Citation behaviour is the source driver of scientific dynamics, and it is essential to understand its effect on knowledge diffusion and intellectual structure. This study explores the effect of citation behaviour on disciplinary knowledge diffusion and intellectual structure by comparing three types of citation behaviour trends, namely the high citation trend, medium citation trend, and low citation trend. The diffusion power, diffusion speed, and diffusion breadth were calculated to quantify knowledge diffusion. The properties of the global and local citation network structure were used to reflect the particular influences of citation behaviour on the scientific intellectual structure. The primary empirical results show that (a) the high citation behaviour trend could improve the knowledge diffusion speed for papers with a short citation history span. Additionally, the medium citation trend has the broadest diffusion breadth whereas the low citation behaviour trend might make the citation counts take off for papers with a long citation history span; (b) the high citation trend has a stronger influence and greater control over the intellectual structure, but this relationship is true only for papers with a short or normal citation history span. These findings could play important roles in scientific research evaluation and impact prediction. 相似文献
7.
《Journal of Informetrics》2023,17(1):101356
Scholarly citations – widely seen as tangible measures of the impact and significance of academic papers – guide critical decisions by research administrators and policy makers. The citation distributions form characteristic patterns that can be revealed by big-data analysis. However, the citation dynamics varies significantly among subject areas, countries etc. The problem is how to quantify those differences, separate global and local citation characteristics. Here, we carry out an extensive analysis of the power-law relationship between the total citation count and the h-index to detect a functional dependence among its parameters for different science domains. The results demonstrate that the statistical structure of the citation indicators admits representation by a global scale and a set of local exponents. The scale parameters are evaluated for different research actors – individual researchers and entire countries – employing subject- and affiliation-based divisions of science into domains. The results can inform research assessment and classification into subject areas; the proposed divide-and-conquer approach can be applied to hidden scales in other power-law systems. 相似文献
8.
This article presents a study that compares detected structural communities in a coauthorship network to the socioacademic characteristics of the scholars that compose the network. The coauthorship network was created from the bibliographic record of a multi-institution, interdisciplinary research group focused on the study of sensor networks and wireless communication. Four different community detection algorithms were employed to assign a structural community to each scholar in the network: leading eigenvector, walktrap, edge betweenness and spinglass. Socioacademic characteristics were gathered from the scholars and include such information as their academic department, academic affiliation, country of origin, and academic position. A Pearson’s χ2test, with a simulated Monte Carlo, revealed that structural communities best represent groupings of individuals working in the same academic department and at the same institution. A generalization of this result suggests that, even in interdisciplinary, multi-institutional research groups, coauthorship is primarily driven by departmental and institutional affiliation. 相似文献
9.
《Journal of Informetrics》2022,16(3):101306
Research on the evaluation of the quality of academic papers is attracting more attention from scholars in scientometrics. However, most previous researches have assessed paper quality based on external indicators, such as citations, which failed to account for the content of the research. To that end, this paper proposed a new method for measuring a paper's originality. The method was based on knowledge units in semantic networks, focusing on the relationship and semantic similarity of different knowledge units. Connectivity and path similarity between different content elements were used in particular networks as indicators of originality. This study used papers published between 2014 and 2018 in three categories (i.e. Library & Information Science, Educational Psychology, and Carbon Nanotubes) and divided their content into three parts (i.e. research topics, research methods and research results). It was found that the originality in all categories increase each year. Furthermore, a comparison of our new method with previous models of citation network analysis and knowledge combination analysis showed that our new method is better than those previous methods when used in measuring originality. 相似文献
10.
Most networks in information science appear as weighted networks, while many of them (e.g. author citation networks, web link networks and knowledge flow networks) are directed networks. Based on the definition of the h-degree, the directed h-degree is introduced for measuring both weighted networks and directed networks. After analyzing the properties and derived measures of the directed h-degree an actual application of LIS journals citation network is worked out. 相似文献
11.
[目的/意义]施引文献与被引文献往往存在着某种相似性,揭示这种现象背后的形成机制有助于深入理解引文的本质。[方法/过程]采用指数随机图模型,以图书馆与情报学领域为对象开展实证分析,旨在揭示文献相似性对引用关系的影响机制。[结果/结论]实证研究发现：在网络结构、机构、期刊层面存在显著的引用文献相似倾向。具体地,引用关系更倾向于嵌入三角传递结构;来源于相同机构和期刊的文献之间更容易产生引用关系;来源于学科优势地位国家的文献之间更容易产生引用。实证结果充分说明社会接近性是引用行为的重要形成机制,反映了引用偏好的社会属性。 相似文献
12.
科学知识扩散研究框架 总被引：2，自引：1，他引：1
通过对科学知识扩散相关文献的梳理,构建科学知识扩散的研究框架,并对研究对象、扩散关系表示、衡量指标、扩散模型等方面进行详细评述。扩散的对象包括期刊、学科、科研人员等,扩散过程主要以文献引证和作者合著关系表示。在实证中,基于文献引证关系的引文及引文网络分析是科学知识扩散的主流研究方法。衡量指标可以按照测度粒度分为文章、期刊、学科3个层次。现有科学知识扩散的模型研究以定性研究为主,定量化分析较少,常见思路为跨学科借鉴成熟模型。 相似文献
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14.
［目的/意义］ 民国时期是中国历史上少有的学术发展活跃期,现阶段有不少民国文献数据库,但都是些面向主题的专题库,而民国学人专题数据库是把基于领域面向主题的数据库升级为以人作为维度的专题数据库,体现出学术交流的特点。［方法/过程］ 从数据库整体框架、功能设计与实现方面介绍民国学人专题数据库,依托民国学人专题数据库,依次从学者组织机构、学术成果合著与引证行为等多维度揭示民国时期学术共同体的特点,最后引入时序分析方法,探究学者的科研行为随时间的变化特点。［结果/结论］ 实证表明,民国时期的学术共同体的变迁受外部政治氛围和内部组织流变影响,而通过对引入时序的学者合著与引证关系的梳理,发现学者对民国时期的学术研究热情持续增进,另外民国期间的学者倾向独立创作,民国之后的学者的合著行为才逐渐频繁。 相似文献
15.
Previous research has shown that citation data from different types of Web sources can potentially be used for research evaluation. Here we introduce a new combined Integrated Online Impact (IOI) indicator. For a case study, we selected research articles published in the Journal of the American Society for Information Science & Technology (JASIST) and Scientometrics in 2003. We compared the citation counts from Web of Science (WoS) and Scopus with five online sources of citation data including Google Scholar, Google Books, Google Blogs, PowerPoint presentations and course reading lists. The mean and median IOI was nearly twice as high as both WoS and Scopus, confirming that online citations are sufficiently numerous to be useful for the impact assessment of research. We also found significant correlations between conventional and online impact indicators, confirming that both assess something similar in scholarly communication. Further analysis showed that the overall percentage for unique Google Scholar citations outside the WoS were 73% and 60% for the articles published in JASIST and Scientometrics, respectively. An important conclusion is that in subject areas where wider types of intellectual impact indicators outside the WoS and Scopus databases are needed for research evaluation, IOI can be used to help monitor research performance. 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.
Chaomei Chen Yue Chen Mark Horowitz Haiyan Hou Zeyuan Liu Donald Pellegrino 《Journal of Informetrics》2009,3(3):191-209
We propose an explanatory and computational theory of transformative discoveries in science. The theory is derived from a recurring theme found in a diverse range of scientific change, scientific discovery, and knowledge diffusion theories in philosophy of science, sociology of science, social network analysis, and information science. The theory extends the concept of structural holes from social networks to a broader range of associative networks found in science studies, especially including networks that reflect underlying intellectual structures such as co-citation networks and collaboration networks. The central premise is that connecting otherwise disparate patches of knowledge is a valuable mechanism of creative thinking in general and transformative scientific discovery in particular. In addition, the premise consistently explains the value of connecting people from different disciplinary specialties. The theory not only explains the nature of transformative discoveries in terms of the brokerage mechanism but also characterizes the subsequent diffusion process as optimal information foraging in a problem space. Complementary to epidemiological models of diffusion, foraging-based conceptualizations offer a unified framework for arriving at insightful discoveries and optimizing subsequent pathways of search in a problem space. Structural and temporal properties of potentially high-impact scientific discoveries are derived from the theory to characterize the emergence and evolution of intellectual networks of a field. Two Nobel Prize winning discoveries, the discovery of Helicobacter pylori and gene targeting techniques, and a discovery in string theory demonstrated such properties. Connections to and differences from existing approaches are discussed. The primary value of the theory is that it provides not only a computational model of intellectual growth, but also concrete and constructive explanations of where one may find insightful inspirations for transformative scientific discoveries. 相似文献
18.
图书馆的查收查引服务,有助于学者及其单位客观认识自身科研成果产出和学术发展。论文结合查收查引工作实践,从数据、算法、交互3个层面分析构建了他引区分策略,并对其进行系统实现。通过随机抽取样本报告做对比验证,实验得出该策略在时间效率和操作便捷性上更具优势,最后针对影响因素进行探讨,希望为学者唯一身份标识领域的研究提供思路参考。 相似文献
19.
学术文献特征表示,是学术文献搜索、分类组织、个性化推荐等学术大数据服务的关键步骤。研究表明,图神经网络能够有效学习文献的特征表示,然而当前研究主要集中在有监督学习方法上,不仅对数据集的大小和质量的要求较高,且学习到的文献特征表示与具体任务高度耦合。基于此,本文将四种无监督图神经网络方法引入学术文献表示学习,从Cora、CiteSeer和DBLP (database systems and logic programming)数据集的引文网络、共被引网络和文献耦合网络中学习文献的表示向量,并应用于文献分类和论文推荐两大下游任务。研究结果表明,(1)深度互信息图神经网络适合于文献分类任务,对抗正则化变分图自编码器则在论文推荐任务上性能更佳;(2)Cora数据集上的结果表明,相较于共被引和文献耦合网络,引文网络更适合于学习通用的文献表示向量。 相似文献
20.
学人研究、学史研究和学论研究是构成档案学研究的三要素,三者相辅相承,共同构建具有中国特色的档案学。只有进行档案学术研究活动,并且取得了一定研究成果的人才是档案学人。研究中国档案学人具有四个意义:对档案学人的研究有助于深入了解档案学发展的历史;可以把对档案学人的研究作为档案学史研究的一个特殊视角;对档案学人的研究能够评估和定位档案学术的发展阶段;研究中国档案学人,是打造中国档案学派的重要路径。研究中国档案学人的四个路径,即"个案研究""比较研究""群体研究"和"学术著作评价"。 相似文献