PageRank variants in the evaluation of citation networks

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.     

Exploring all-author tripartite citation networks: A case study of gene editing

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.     

A new approach to the metric of journals’ scientific prestige: The SJR indicator

A size-independent indicator of journals’ scientific prestige, the SCImago Journal Rank (SJR) indicator, is proposed that ranks scholarly journals based on citation weighting schemes and eigenvector centrality. It is designed for use with complex and heterogeneous citation networks such as Scopus. Its computation method is described, and the results of its implementation on the Scopus 2007 dataset is compared with those of an ad hoc Journal Impact Factor, JIF(3y), both generally and within specific scientific areas. Both the SJR indicator and the JIF distributions were found to fit well to a logarithmic law. While the two metrics were strongly correlated, there were also major changes in rank. In addition, two general characteristics were observed. On the one hand, journals’ scientific influence or prestige as computed by the SJR indicator tended to be concentrated in fewer journals than the quantity of citation measured by JIF(3y). And on the other, the distance between the top-ranked journals and the rest tended to be greater in the SJR ranking than in that of the JIF(3y), while the separation between the middle and lower ranked journals tended to be smaller.     

面向引用关系的引文内容标注框架研究

引文内容分析能够帮助揭示文献引用关系的深层语义内涵。本文梳理了目前已有的引文内容标注体系,归纳出构建引文分类体系的三个主要维度,即引文功能,引文重要性,情感倾向。以支持文献引用关系分析为目标,针对引文内容分析设计出一个引文内容标注框架,其中包括揭示引文关系抽象性质的引文分类标注体系,描述被引文献具体内容的引用对象标注体系,以及记录引文客观特征的引文属性标注体系。具体的标注实验体现了该标注框架的可用性。图1。表6。参考文献56。     

Combining mapping and citation network analysis for a better understanding of the scientific development: The case of the absorptive capacity field

The general aim of this paper is to show the results of a study in which we combined bibliometric mapping and citation network analysis to investigate the process of creation and transfer of knowledge through scientific publications. The novelty of this approach is the combination of both methods. In this case we analyzed the citations to a very influential paper published in 1990 that contains, for the first time, the term Absorptive Capacity. A bibliometric map identified the terms and the theories associated with the term while two techniques from the citation network analysis recognized the main papers during 15 years. As a result we identified the articles that influenced the research for some time and linked them into a research tradition that can be considered the backbone of the “Absorptive Capacity Field”.     

Improving the reliability of short-term citation impact indicators by taking into account the correlation between short- and long-term citation impact

The normalized citation indicator may not be sufficiently reliable when a short citation time window is used, because the citation counts for recently published papers are not as reliable as those for papers published many years ago. In a limited time period, recent publications usually have insufficient time to accumulate citations and the citation counts of these publications are not sufficiently reliable to be used in the citation impact indicators. However, normalization methods themselves cannot solve this problem. To solve this problem, we introduce a weighting factor to the commonly used normalization indicator Category Normalized Citation Impact (CNCI) at the paper level. The weighting factor, which is calculated as the correlation coefficient between citation counts of papers in the given short citation window and those in the fixed long citation window, reflects the degree of reliability of the CNCI value of one paper. To verify the effect of the proposed weighted CNCI indicator, we compared the CNCI score and CNCI ranking of 500 universities before and after introducing the weighting factor. The results showed that although there was a strong positive correlation before and after the introduction of the weighting factor, some universities’ performance and rankings changed dramatically.     

Optimizing SCImago Journal & Country Rank classification by community detection

Subject classification arises as an important topic for bibliometrics and scientometrics, searching to develop reliable and consistent tools and outputs. Such objectives also call for a well delimited underlying subject classification scheme that adequately reflects scientific fields. Within the broad ensemble of classification techniques, clustering analysis is one of the most successful.Two clustering algorithms based on modularity – the VOS and Louvain methods – are presented here for the purpose of updating and optimizing the journal classification of the SCImago Journal & Country Rank (SJR) platform. We used network analysis and Pajek visualization software to run both algorithms on a network of more than 18,000 SJR journals combining three citation-based measures of direct citation, co-citation and bibliographic coupling. The set of clusters obtained was termed through category labels assigned to SJR journals and significant words from journal titles.Despite the fact that both algorithms exhibited slight differences in performance, the results show a similar behaviour in grouping journals. Consequently, they are deemed to be appropriate solutions for classification purposes. The two newly generated algorithm-based classifications were compared to other bibliometric classification systems, including the original SJR and WoS Subject Categories, in order to validate their consistency, adequacy and accuracy. In addition to some noteworthy differences, we found a certain coherence and homogeneity among the four classification systems analysed.     

Identifying missing relevant patent citation links by using bibliographic coupling in LED illuminating technology

This study uses bibliographic coupling to identify missing relevant patent links, in order to construct a comprehensive citation network. Missing citation links can be added by taking the missing relevant patent links into account. The Pareto principle is used to determine the threshold of bibliographic coupling strength, in order to identify the missing relevant patent links. Comparisons between the original patent citation network and the comprehensive patent citation network with the missing relevant patent links are illustrated at both the patent and assignee levels. Light emitting diode (LED) illuminating technology is chosen as the case study. The relationships between the patents and the assignees are obviously enhanced after adding the missing relevant patent links. The results show that the growth rates on both the total number and the average number of links have apparently improved at the patent level. At the assignee level, the number of linked assignees and the average number of links between two assignees are increased. The differences between the two citation networks are further examined by means of the Freeman vertex betweenness centrality and Johnson's hierarchical clustering. The patents with more new links to other patents have distinct results in terms of the Freeman vertex betweenness centrality. The enhancement of links among patents also results in different clustering.     

Measuring the academic reputation through citation networks via PageRank

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.     

Generalization of bibliographic coupling and co-citation using the node split network

Bibliographic coupling (BC) and co-citation (CC) are the two most common citation-based coupling measures of similarity between scientific items. One can interpret these measures as second-neighbor relations distinguished by the direction of the citation: BC is a similarity between two citing items, whereas CC is that between two cited items. A previous study proposed a two-layer node split network that can emulate clusters of coupling measures in a computationally efficient manner; however, the lack of intralayer links makes it impossible to obtain exact similarities. Here, we propose novel methods to estimate intralayer similarity on a node split network using personalized PageRank (PPR) and neural embedding (EMB). We demonstrate that PPR is strongly correlated with the coupling measures. Moreover, our proposed method can yield precise similarities between items even if they are distant from each other. We also show that many links with high similarity are missing in the original BC/CC network, which suggests that it is essential to consider long-range similarities. Comparative experiments on global and local edge sampling suggest that local sampling is stable for PPR in node split networks. This analysis offers valuable insights into the process of searching for significantly related items regarding each coupling measure.     

Developing a four-entities reinforced rank model to evaluate the topic influence in academic networks

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.     

Bi-directional h-index: A new measure of node centrality in weighted and directed networks

This paper builds an index family, named bi-directional h-index, to measure node centrality in weighted directed networks. Bi-directional h-index takes the directed degree centrality as the initial value and iteratively uses more network information to update the node’s importance. We prove the convergence of the iterative process after finite iterations and introduce an asynchronous updating process that provides a decentralized, local method to calculate the bi-directional h-index in large-scale networks and dynamic networks. The theoretical analysis manifests that the bi-directional h-index is feasible and significant for establishing a greater conceptual framework that includes some existing index concepts, such as lobby index, node’s h-index, c-index and iterative c-index. An example using journal citation networks indicates that the bi-directional h-index is different from directed degree centrality, directed node strength, directed h-degree and the HITS algorithm in ranking node importance. It is irreplaceable and can reflect these measures of node’s importance.     

基于样本加权的引文网络的社团划分

[目的/意义] 为提高引文网络的社团划分的准确性,提出一种基于加权的引文网络的社团划分方法。[方法/过程] 以Louvain社团划分方法为算法基础,将科学论文用向量空间模型表示,利用改进的余弦相似度方法计算相邻论文之间的相似度,并将其作为权重,综合考虑论文内容属性与结构属性,提出一种基于样本加权的引文网络社团划分方法。[结果/结论] 该算法将引文网络中论文的文本内容属性与拓扑结构属性结合起来,通过对Scientometrics期刊发表的论文以及主题为CRISPR的论文进行社团划分研究实验,结果表明该方法能改善引文网络社团的划分效果。     

试论引文分析方法的网络化发展与应用

引文分析是传统文献计量学和科学计量学的一种独特研究方法。主要从网络链接分析研究、基于网页链接分析的搜索引擎排序算法研制和新型网络引文索引工具的编制等方面,分析论述引文分析方法在网络环境下的发展和应用,以期形成对引文分析方法及其价值的合理认知和评价。     

专利引用关系形成的解释框架:一个指数随机图模型视角

[目的/意义]近年来，围绕着专利引文网络结构特征的研究出现大量的研究成果，这些成果都从某种程度上折射出专利引文关系的形成受到来自属性特征之外关系特征的影响，而现有的以回归方法为基础的统计推断方法难以将这些因素纳入到分析框架中，因此，急需探索新的方法。[方法/过程]从关系形成视角，专利引用关系形成可表示三种广义的关系形成过程：自组织影响过程、自身属性影响过程、网络协变量影响过程，并建立关系形成过程与网络配置间的映射关系，最终，形成一整套可用于理解复杂专利引用关系形成问题的解释框架。[结果/结论]提出一整套可用于理解复杂专利引用关系形成问题的解释框架，该框架是未来进一步构建网络统计模型的理论基础，另外，解释框架包含丰富的网络配置项，预示着未来指数随机图模型在文献计量、科学网络分析上广阔的应用前景。     

基于关联数据的科研机构评价指标权重计算方法研究

[目的/意义] 科研评价中指标权重计算的合理性将直接影响科研机构评价结果的客观性和准确性,本文提出利用信息论方法来计算指标权重,为指标权重计算提供一种新的思路。[方法/过程] 基于DBpedia数据集,利用信息论方法计算出的指标权重和上海交通大学世界大学排行榜已有的指标权重,同时对榜单前100名大学机构进行排名,并将排名结果进行对比分析。[结果/结论] 实验发现利用本文提出的权重计算方法得出的机构得分结果与上海交通大学已有指标权重的得分结果皮尔逊相关性为0.980,斯皮尔曼相关性为0.939,并且其排名顺序和上海交通大学给出的排名顺序皮尔逊相关性和斯皮尔曼相关性均为0.939。以上两个排名结果的得分相关性和排名相关性极强,证明本研究中关联数据的权重计算方法的有效性。     

Predicting scientific research trends based on link prediction in keyword networks

The rapid development of scientific fields in this modern era has raised the concern for prospective scholars to find a proper research field to conduct their future studies. Thus, having a vision of future could be helpful to pick the right path for doing research and ensuring that it is worth investing in. In this study, we use article keywords of computer science journals and conferences, assigned by INSPEC controlled indexing, to construct a temporal scientific knowledge network. By observing keyword networks snapshots over time, we can utilize the link prediction methods to foresee the future structures of these networks. We use two different approaches for this link prediction problem. First, we have utilized three topology-based link prediction algorithms, two of which are commonly used in literature. We have also proposed a third algorithm based on nodes (keywords) clustering coefficient, their centrality measures like eigenvector centrality, and nodes community information. Then, we used nodes topological features and the outputs of aforementioned topology-based link prediction algorithms as features to feed five machine learning link prediction algorithms (SVM, Random Forest Classifier, K-Nearest Neighbors, Gaussian Naïve Bayes, and Multinomial Naïve Bayes). All tested predictors have shown considerable performance and their results are discussed in this paper.     

科研领域重点研究方向判别方法研究

[目的/意义] 判别重点研究方向对科研管理和科技政策的制定有着重要参考价值,已有的定量方法多是根据创新性、新颖性以及增长性等特征属性设计指标进行识别、推荐,本研究进一步利用研究方向间的关联关系,从网络拓扑结构和特征属性两个维度判别重点研究方向。[方法/过程] 在构建领域文献引用网络的基础上,利用大规模网络聚类算法识别研究方向,并构建研究方向关联网络,利用网络重要节点识别算法从网络拓扑结构的角度判别重点研究方向,同时结合新颖性、增长性和H指数三个特征属性指标,构建了重点研究方向遴选指标体系。[结果/结论] 对纳米科技领域进行实证分析,经专家判读,认为加权PageRank、Gefura以及增长性指标更加具有客观性、全面性和稳定性,通过综合运用三个指标遴选出208个纳米科技领域的重点研究方向。     

Collaboration prediction based on multilayer all-author tripartite citation networks: A case study of gene editing

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.     

The development of a scientometric database for monitoring the publication activities of Russian scientists at the Mendeleyev University of Chemical Technology of Russia

The existing approaches to the definition of the scientific contributions made by researchers are analyzed. A bibliometric database is developed on the basis of the quantitative analysis of publication activities monitored by the most representative global citation systems, such as the Web of Science (Thomson Reuters, USA), Scopus (Elsevier, the Netherlands), and the Russian Science Citation Index (Scientific Electronic Library, the Russian Federation). The system allows teachers and researchers to consult their scientific publications (contained in Scopus, the WoS, and the RSCI),check citation levels and the h-index, filter data by the date of publication, and access the profiles of other researchers.