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Journal articles on the topic 'Citation networks'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 February 2022

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1

Bu, Yi, Yong Huang, and Wei Lu. "Loops in publication citation networks." Journal of Information Science 46, no. 6 (September 6, 2019): 837–48. http://dx.doi.org/10.1177/0165551519871826.

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Traditionally, publication citation networks are regarded as acyclic, that is, no loops in the network as an earlier published article cannot cite a later published article. However, due to the accessibility of pre-print versions of articles, there might be some loops in a publication citation network. This article presents a descriptive statistic on loops in publication citation networks of computer science and physics by employing a network-based indicator, namely, strongly connected component (SCC). By employing computer science and physics disciplines publications from the Web of Science database as examples, this article examines the count of loops, how the count changes over time and how the count relates to the published year difference between publications within the loop in the citation network. Some common structural patterns are also extracted and analysed; we observe that the two disciplines share the most frequent patterns though there exist some minor differences. Moreover, we find that self-citations in terms of authors, authors’ institutions and journals contribute to the formation of loops in publication citation networks.

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Wen, Fangfang. "Study on the research evolution of Nobel laureates 2018 based on self-citation network." Journal of Documentation 75, no. 6 (September 26, 2019): 1416–31. http://dx.doi.org/10.1108/jd-02-2019-0027.

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Purpose Science is a continuum of experiences consisting of authors and their publications, and the authors’ experience is an integral part of their work that gets reflected through self-citations. Thus, self-citations can be employed in measuring the relevance between publications and tracking the evolution of research. The paper aims to discuss this issue. Design/methodology/approach Based on the bibliographic data obtained from Scopus, this study constructs and visualizes the self-citation networks of ten Nobel laureates 2018, in the fields of Physiology or Medicine, Physics, Chemistry and Economic Science, to demonstrate the evolving process of each laureate’s research across his or her scholarly career. Findings Statistics indicate that prominent scientists, such as Nobel laureates, have also frequently cited their own publications. However, their self-cited rates are quite low. Self-citations constitute an indispensable part of the citation system but contribute little to authors’ scientific impact, regardless of artificial self-citations. Self-citation networks present a trajectory that shows the evolving process of research across a scientist’s long-term scholarly career. There are obvious differences in self-citation patterns and network structures of different laureates without a disciplinary difference observed. The structures of self-citation networks are significantly influenced by laureates’ productivity. In addition, it is laureates’ own research patterns and citation habits that lead to the diversified patterns and structures of self-citation networks. Research limitations/implications Only scientific achievements presented in the form of publications are investigated and other kinds of scientific output, such as patents, are not included. Moreover, this approach is fit for scientists who have had a longer career and higher productivity. Originality/value This study proves the feasibility and effectiveness of self-citation analysis as a new way to examine research evolution.

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Goldberg, S. R., H. Anthony, and T. S. Evans. "Modelling citation networks." Scientometrics 105, no. 3 (September 5, 2015): 1577–604. http://dx.doi.org/10.1007/s11192-015-1737-9.

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Chakraborty, Manajit, Maksym Byshkin, and Fabio Crestani. "Patent citation network analysis: A perspective from descriptive statistics and ERGMs." PLOS ONE 15, no. 12 (December 3, 2020): e0241797. http://dx.doi.org/10.1371/journal.pone.0241797.

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Patent Citation Analysis has been gaining considerable traction over the past few decades. In this paper, we collect extensive information on patents and citations and provide a perspective of citation network analysis of patents from a statistical viewpoint. We identify and analyze the most cited patents, the most innovative and the highly cited companies along with the structural properties of the network by providing in-depth descriptive analysis. Furthermore, we employ Exponential Random Graph Models (ERGMs) to analyze the citation networks. ERGMs enables understanding the social perspectives of a patent citation network which has not been studied earlier. We demonstrate that social properties such as homophily (the inclination to cite patents from the same country or in the same language) and transitivity (the inclination to cite references’ references) together with the technicalities of the patents (e.g., language, categories), has a significant effect on citations. We also provide an in-depth analysis of citations for sectors in patents and how it is affected by the size of the same. Overall, our paper delves into European patents with the aim of providing new insights and serves as an account for fitting ERGMs on large networks and analyzing them. ERGMs help us model network mechanisms directly, instead of acting as a proxy for unspecified dependence and relationships among the observations.

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Du, San Shan, and Yue Chun Wu. "Research Paper Influence Measurement and Applications: A Machine-Learning-Based Approach." Advanced Materials Research 1049-1050 (October 2014): 2073–78. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.2073.

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Measuring the influence of academic research publication is an meaningful work in academe. In this paper, the co-author and the citation networks are built to calculate the influence of a researcher and a paper in the way of networks separately with the discussion of further applications. At the beginning, the co-author network is built to determine the influence of co-authors. Then, based on the citations among the papers in the database, we build up the citation network with the help of graph theory. Thirdly, the method is implemented with the application of American Airline network analysis. As the final, the analysis of strengths and weaknesses is conducted.

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6

Souma, Wataru, Irena Vodenska, and Lou Chitkushev. "Classification of Paper Values Based on Citation Rank and PageRank." Journal of Data and Information Science 5, no. 3 (July 28, 2020): 57–70. http://dx.doi.org/10.2478/jdis-2020-0031.

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AbstractPurposeThe number of citations has been widely used to measure the significance of a paper. However, there is a need in introducing another index to determine superiority or inferiority of papers with the same number of citations. We determine superiority or inferiority of papers by using the ranking based on the number of citations and PageRank.Design/methodology/approachWe show the positive linear correlation between Citation Rank (the ranking of the number of citation) and PageRank. On this basis, we identify high-quality, prestige, emerging, and popular papers.FindingsWe found that the high-quality papers belong to the subjects of biochemistry and molecular biology, chemistry, and multidisciplinary sciences. The prestige papers correspond to the subjects of computer science, engineering, and information science. The emerging papers are related to biochemistry and molecular biology, as well as those published in the journal “Cell.” The popular papers belong to the subject of multidisciplinary sciences.Research limitationsWe analyze the Science Citation Index Expanded (SCIE) from 1981 to 2015 to calculate Citation Rank and PageRank within a citation network consisting of 34,666,719 papers and 591,321,826 citations.Practical implicationsOur method is applicable to forecast emerging fields of research subjects in science and helps policymakers to consider science policy.Originality/valueWe calculated PageRank for a giant citation network which is extremely larger than the citation networks investigated by previous researchers.

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7

Hajra, Kamalika Basu, and Parongama Sen. "Aging in citation networks." Physica A: Statistical Mechanics and its Applications 346, no. 1-2 (February 2005): 44–48. http://dx.doi.org/10.1016/j.physa.2004.08.048.

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Henrique, Bruno Miranda, Vinicius Amorim Sobreiro, and Herbert Kimura. "Building direct citation networks." Scientometrics 115, no. 2 (February 20, 2018): 817–32. http://dx.doi.org/10.1007/s11192-018-2676-z.

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Hu, Feng, Lin Ma, Xiu-Xiu Zhan, Yinzuo Zhou, Chuang Liu, Haixing Zhao, and Zi-Ke Zhang. "The aging effect in evolving scientific citation networks." Scientometrics 126, no. 5 (March 12, 2021): 4297–309. http://dx.doi.org/10.1007/s11192-021-03929-8.

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AbstractThe study of citation networks is of interest to the scientific community. However, the underlying mechanism driving individual citation behavior remains imperfectly understood, despite the recent proliferation of quantitative research methods. Traditional network models normally use graph theory to consider articles as nodes and citations as pairwise relationships between them. In this paper, we propose an alternative evolutionary model based on hypergraph theory in which one hyperedge can have an arbitrary number of nodes, combined with an aging effect to reflect the temporal dynamics of scientific citation behavior. Both theoretical approximate solution and simulation analysis of the model are developed and validated using two benchmark datasets from different disciplines, i.e. publications of the American Physical Society (APS) and the Digital Bibliography & Library Project (DBLP). Further analysis indicates that the attraction of early publications will decay exponentially. Moreover, the experimental results show that the aging effect indeed has a significant influence on the description of collective citation patterns. Shedding light on the complex dynamics driving these mechanisms facilitates the understanding of the laws governing scientific evolution and the quantitative evaluation of scientific outputs.

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WANG, MINGYANG, GUANG YU, and DAREN YU. "THE PREFERENTIAL ATTACHMENT MECHANISM BASING ON WEIGHTED PAST CITATIONS." International Journal of Modern Physics B 25, no. 15 (June 20, 2011): 2055–61. http://dx.doi.org/10.1142/s0217979211100424.

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In this paper, we investigate the influence of past citations ki on papers' attachment rate Π(k, t) and propose a method to consider these influences by assigning different weights to ki. The correlation r between Π(k, t) and ki decrease rapidly with time, which suggests the different influences of past citations on the preferential attachment in citation networks. According to the different influences of ki on Π(k, t), we put different weights to ki and find a good linear dependence between Π(k, t) and the weighted past citations in three actual citation networks.

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Liu, Yuxian, and Ronald Rousseau. "Ego Citation Networks Considered as Domination Networks." Journal of Scientometric Research 8, no. 1 (May 1, 2019): 01–08. http://dx.doi.org/10.5530/jscires.8.1.1.

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12

Lewis, Jonathan. "How Transdisciplinary is Design? An Analysis Using Citation Networks." Design Issues 36, no. 1 (January 2020): 30–44. http://dx.doi.org/10.1162/desi_a_00573.

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This article reveals how citation networks can be used to understand and demonstrate the ways design serves to integrate other bodies of knowledge. Analysis focused on two corpuses of peer-reviewed journal articles, representing two bodies of knowledge. The first corpus consisted of 277 articles with the words “Design Thinking” in title. The second corpus contained 296 articles with the words “Network Science” in the title. Paper citation and document co-citation networks of individual and combined corpuses were analyzed. The overlap of paper citation networks was used to synthesize similarities, differences, and opportunities for further integration.

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Clough, J. R., J. Gollings, T. V. Loach, and T. S. Evans. "Transitive reduction of citation networks." Journal of Complex Networks 3, no. 2 (September 16, 2014): 189–203. http://dx.doi.org/10.1093/comnet/cnu039.

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Shibata, Naoki, Yuya Kajikawa, and Ichiro Sakata. "Link prediction in citation networks." Journal of the American Society for Information Science and Technology 63, no. 1 (October 13, 2011): 78–85. http://dx.doi.org/10.1002/asi.21664.

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15

Greenberg, Steven A. "Understanding belief using citation networks." Journal of Evaluation in Clinical Practice 17, no. 2 (February 23, 2011): 389–93. http://dx.doi.org/10.1111/j.1365-2753.2011.01646.x.

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Shepherd, Michael A., C. R. Watters, and Yao Cai. "Transient hypergraphs for citation networks." Information Processing & Management 26, no. 3 (January 1990): 395–412. http://dx.doi.org/10.1016/0306-4573(90)90099-n.

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Takeda, Yoshiyuki, and Yuya Kajikawa. "Tracking modularity in citation networks." Scientometrics 83, no. 3 (January 29, 2010): 783–92. http://dx.doi.org/10.1007/s11192-010-0158-z.

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Hu, Xiaojun, Ronald Rousseau, and Jin Chen. "Structural indicators in citation networks." Scientometrics 91, no. 2 (December 31, 2011): 451–60. http://dx.doi.org/10.1007/s11192-011-0587-3.

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WANG, XUE-WEN, LI-JIE ZHANG, GUO-HONG YANG, and XIN-JIAN XU. "MODELING CITATION NETWORKS BASED ON VIGOROUSNESS AND DORMANCY." Modern Physics Letters B 27, no. 22 (August 20, 2013): 1350155. http://dx.doi.org/10.1142/s0217984913501558.

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In citation networks, the activity of papers usually decreases with age and dormant papers may be discovered and become fashionable again. To model this phenomenon, a competition mechanism is suggested which incorporates two factors: vigorousness and dormancy. Based on this idea, a citation network model is proposed, in which a node has two discrete stage: vigorous and dormant. Vigorous nodes can be deactivated and dormant nodes may be activated and become vigorous. The evolution of the network couples addition of new nodes and state transitions of old ones. Both analytical calculation and numerical simulation show that the degree distribution of nodes in generated networks displays a good right-skewed behavior. Particularly, scale-free networks are obtained as the deactivated vertex is target selected and exponential networks are realized for the random-selected case. Moreover, the measurement of four real-world citation networks achieves a good agreement with the stochastic model.

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Martinez-Perez, Clara, Cristina Alvarez-Peregrina, Cesar Villa-Collar, and Miguel Ángel Sánchez-Tena. "Current State and Future Trends: A Citation Network Analysis of the Academic Performance Field." International Journal of Environmental Research and Public Health 17, no. 15 (July 24, 2020): 5352. http://dx.doi.org/10.3390/ijerph17155352.

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Background: In recent years, due to its complexity and relevance, academic performance has become a controversial research topic within the health and educational field. The main purposes of this study were to analyze the links between publications and authors via citation networks, to identify the different research areas and to determine the most cited publications. Methods: The publication search was performed through the Web of Science database, using the term “Academic Performance” for a time interval from 1952 to 2019. The software used to analyze the publications was the Citation Network Explorer. Results: We found a total of 16,157 publications with 35,213 citations generated in the network, and 2018 had the highest number of publications of any year. The most cited publication was published in 2012 by Richardson et al. with a citation index score of 352. By using the clustering function, we found nine groups related to different areas of research in this field: health, psychology, psychosociology, demography, physical activity, sleep patterns, vision, economy, and delinquency. Conclusions: The citation network showed the main publications dealing with the different factors that affect academic performance, and it was determined that psychological and psychosocial factors were the most relevant.

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Zingg, Christian, Vahan Nanumyan, and Frank Schweitzer. "Citations driven by social connections? A multi-layer representation of coauthorship networks." Quantitative Science Studies 1, no. 4 (December 2020): 1493–509. http://dx.doi.org/10.1162/qss_a_00092.

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To what extent is the citation rate of new papers influenced by the past social relations of their authors? To answer this question, we present a data-driven analysis of nine different physics journals. Our analysis is based on a two-layer network representation constructed from two large-scale data sets, INSPIREHEP and APS. The social layer contains authors as nodes and coauthorship relations as links. This allows us to quantify the social relations of each author, prior to the publication of a new paper. The publication layer contains papers as nodes and citations between papers as links. This layer allows us to quantify scientific attention as measured by the change of the citation rate over time. We particularly study how this change correlates with the social relations of their authors, prior to publication. We find that on average the maximum value of the citation rate is reached sooner for authors who have either published more papers or who have had more coauthors in previous papers. We also find that for these authors the decay in the citation rate is faster, meaning that their papers are forgotten sooner.

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22

Daud, Ali, Waqas Ahmed, Tehmina Amjad, Jamal Abdul Nasir, Naif Radi Aljohani, Rabeeh Ayaz Abbasi, and Ishfaq Ahmad. "Who will cite you back? Reciprocal link prediction in citation networks." Library Hi Tech 35, no. 4 (November 20, 2017): 509–20. http://dx.doi.org/10.1108/lht-02-2017-0044.

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Purpose Link prediction in social networks refers toward inferring the new interactions among the users in near future. Citation networks are constructed based on citing each other papers. Reciprocal link prediction in citations networks refers toward inferring about getting a citation from an author, whose work is already cited by you. The paper aims to discuss these issues. Design/methodology/approach In this paper, the authors study the extent to which the information of a two-way citation relationship (called reciprocal) is predictable. The authors propose seven different features based on papers, their authors and citations of each paper to predict reciprocal links. Findings Extensive experiments are performed on CiteSeer data set by using three classification algorithms (decision trees, Naive Bayes, and support vector machines) to analyze the impact of individual, category wise and combination of features. The results reveal that it is likely to precisely predict 96 percent of reciprocal links. The study delivers convincing evidence of presence of the underlying equilibrium amongst reciprocal links. Research limitations/implications It is not a generic method for link prediction which can work for different networks with relevant features and parameters. Practical implications This paper predicts the reciprocal links to show who is citing your work to collaborate with them in future. Social implications The proposed method will be helpful in finding collaborators and developing academic links. Originality/value The proposed method uses reciprocal link prediction for bibliographic networks in a novel way.

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Zhao, Weidong, Zhaoxin Yu, and Ran Wu. "A citation recommendation method based on context correlation." Intelligent Data Analysis 25, no. 1 (January 26, 2021): 225–43. http://dx.doi.org/10.3233/ida-195041.

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Researchers need to formulate their achievements as research papers. Representative references are essential to high-quality papers. Academic citation recommendation refers to providing the recommendation of citations for the author of papers when they write. With the help of citation recommendation, researchers can improve the efficiency of writing academic papers and reduce the omission of important related literature. To achieve this goal, some methods were proposed. Many of them used citation networks to learn the representation of papers and chose references, they tended to ignore the content properties of papers. There are also some methods used partial properties to recommend citation. But their performance can be further improved. In this paper, we propose a citation recommendation method based on context correlation. We use two neural network models to learn the representations of papers and their references, then calculate the context similarity of them. Besides, we also introduce the publishing time and authority of papers, two key properties of papers for citation evaluation. In the experiment section, we compare our method with other methods and evaluate the performance of different properties choice in our method, it shows that our method outperforms some baselines and the combination of the dimensions including time, authority and context performs better.

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Zia, Muhammad Azam, Zhongbao Zhang, Guangda Li, Haseeb Ahmad, and Sen Su. "Prediction of Rising Venues in Citation Networks." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 4 (July 20, 2017): 650–58. http://dx.doi.org/10.20965/jaciii.2017.p0650.

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Prediction of rising stars has become a core issue in data mining and social networks. Prediction of rising venues could unveil rapidly emerging research venues in citation network. The aim of this research is to predict the rising venues. First, we presented five effective prediction features along with their mathematical formulations for extracting rising venues. The underlying features are composed by incorporating the citation count, publications, cited to and cited by information at venue level. For prediction purpose, we employ four machine learning algorithms including Bayesian Network, Support Vector Machine, Multilayer Perceptron and Random Forest. Experimental results demonstrate that proposed features set are effective for rising venues prediction. Our empirical analysis spotlights the rising venues that demonstrate the continuous improvement over time and finally become the leading scientific venues.

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Mones, Enys, Péter Pollner, and Tamás Vicsek. "Universal hierarchical behavior of citation networks." Journal of Statistical Mechanics: Theory and Experiment 2014, no. 5 (May 22, 2014): P05023. http://dx.doi.org/10.1088/1742-5468/2014/05/p05023.

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Hajra, Kamalika Basu, and Parongama Sen. "Modelling aging characteristics in citation networks." Physica A: Statistical Mechanics and its Applications 368, no. 2 (August 2006): 575–82. http://dx.doi.org/10.1016/j.physa.2005.12.044.

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Bommarito, Michael J., Daniel Martin Katz, Jonathan L. Zelner, and James H. Fowler. "Distance measures for dynamic citation networks." Physica A: Statistical Mechanics and its Applications 389, no. 19 (October 2010): 4201–8. http://dx.doi.org/10.1016/j.physa.2010.06.003.

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Ren, Fu-Xin, Hua-Wei Shen, and Xue-Qi Cheng. "Modeling the clustering in citation networks." Physica A: Statistical Mechanics and its Applications 391, no. 12 (June 2012): 3533–39. http://dx.doi.org/10.1016/j.physa.2012.02.001.

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Ji, Pengsheng, and Jiashun Jin. "Coauthorship and citation networks for statisticians." Annals of Applied Statistics 10, no. 4 (December 2016): 1779–812. http://dx.doi.org/10.1214/15-aoas896.

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Zhao, Dangzhi, and Andreas Strotmann. "Analysis and Visualization of Citation Networks." Synthesis Lectures on Information Concepts, Retrieval, and Services 7, no. 1 (February 7, 2015): 1–207. http://dx.doi.org/10.2200/s00624ed1v01y201501icr039.

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Hurter, Christophe. "Analysis and Visualization of Citation Networks." Synthesis Lectures on Visualization 3, no. 2 (December 27, 2015): 1–127. http://dx.doi.org/10.2200/s00688ed1v01y201512vis006.

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Bi, Henry H., Jianrui Wang, and Dennis K. J. Lin. "Comprehensive Citation Index for Research Networks." IEEE Transactions on Knowledge and Data Engineering 23, no. 8 (August 2011): 1274–78. http://dx.doi.org/10.1109/tkde.2010.167.

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Huo, Dongxia, and Saranan Photchanachan. "Bibliometric Analysis of Knowledge Networks and Creativity." International Journal of Business and Management 16, no. 5 (April 2, 2021): 35. http://dx.doi.org/10.5539/ijbm.v16n5p35.

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Despite the sharp increase in awareness of workplace knowledge networks and creativity research, no attention has been paid to objectively visualizing the evolution of this fast-growing area to complement prior qualitative reviews. This bibliometric analysis involves an examination of 341 global knowledge networks and creativity articles in management-related research in the Web of Science database. Using CiteSpace V visualization literature measurement software, the knowledge map of the knowledge networks and creativity research was drawn using the scientific metrology knowledge graph research method. We conduct Publication time analysis, country/region analysis, journal co-citation analysis, author co-citation, document co-citation, the time-zone visualization analysis on research literature in the field of knowledge networks and creativity, the major researchers and topics in the field of knowledge network, and creativity are clearly presented. Moreover, we summarize the developing trends of knowledge networks and creativity research. On this basis, our review demonstrates the systematic development of literature and identifies trends to advance knowledge networks and creativity research.

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RICE, RONALD E., CHRISTINE L. BORGMAN, and BYRON REEVES. "Citation Networks of Communication Journals, 1977?1985 Cliques and Positions, Citations Made and Citations Received." Human Communication Research 15, no. 2 (December 1988): 256–83. http://dx.doi.org/10.1111/j.1468-2958.1988.tb00184.x.

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Nascimento, Henrique, Clara Martinez-Perez, Cristina Alvarez-Peregrina, and Miguel Ángel Sánchez-Tena. "Citations Network Analysis of Vision and Sport." International Journal of Environmental Research and Public Health 17, no. 20 (October 18, 2020): 7574. http://dx.doi.org/10.3390/ijerph17207574.

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Background: Sports vision is a relatively new specialty, which has attracted particular interest in recent years from trainers and athletes, who are looking at ways of improving their visual skills to attain better performance on the field of play. The objective of this study was to use citation networks to analyze the relationships between the different publications and authors, as well as to identify the different areas of research and determine the most cited publication. Methods: The search for publications was carried out in the Web of Science database, using the terms “sport”, “vision”, and “eye” for the period between 1911 and August 2020. The publication analysis was performed using the Citation Network Explorer and CiteSpace software. Results: In total, 635 publications and 801 citations were found across the network, with 2019 being the year with the highest number of publications. The most cited publication was published in 2002 by Williams et al. By using the clustering functionality, four groups covering the different research areas in this field were found: ocular lesion, visual training methods and efficiency, visual fixation training, and concussions. Conclusions: The citation network offers an objective and comprehensive analysis of the main papers on sports vision.

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Ridi, Niccolò. "The Shape and Structure of the ‘Usable Past’: An Empirical Analysis of the Use of Precedent in International Adjudication." Journal of International Dispute Settlement 10, no. 2 (June 1, 2019): 200–247. http://dx.doi.org/10.1093/jnlids/idz007.

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Abstract How do international adjudicators use precedent? This question has been addressed several times in the literature, but doctrinal accounts have generally failed to consider the aggregate dimension of the phenomenon. This article seeks to provide an alternative outlook by offering a large-scale computational analysis of the body of jurisprudence of three international fora (the ICJ, the WTO Appellate Body and investment arbitration tribunals) and comparing their citation patterns with those of other judicial bodies—national and international. Building on a very large dataset (comprising over 200,000 citations), it employs network analysis tools to measure the evolution of international law citation networks. It then unpacks this emerging complexity by considering what, in a precedent, holds ‘citing value’, highlighting the expansion of the range of precedential resources as well as the consecration of established authorities. Finally, the article considers three examples of computational analysis of citations to precedent in order to better gauge the level of engagement with the past.

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DE BENEDICTIS, LUCA, MARIA PROSPERINA VITALE, and STANLEY WASSERMAN. "Examining the literature on “Networks in Space and in Time.” An introduction." Network Science 3, no. 1 (March 2015): 1–17. http://dx.doi.org/10.1017/nws.2015.13.

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AbstractThe special issue of “Networks in space and in time: methods and applications” contributes to the debate on contextual analysis in network science. It includes seven research papers that shed light on the analysis of network phenomena studied within geographic space and across temporal dimensions. In these papers, methodological issues as well as specific applications are described from different fields. We take the seven papers, study their citations and texts, and relate them to the broader literature. By exploiting the bibliographic information and the textual data of these seven documents, citation analysis and lexical correspondence analysis allow us to evaluate the connections among the papers included in this issue.

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Yu, So-Young. "Curve Estimation among Citation and Centrality Measures in Article-level Citation Networks." Journal of the Korean Society for information Management 29, no. 2 (June 30, 2012): 193–204. http://dx.doi.org/10.3743/kosim.2012.29.2.193.

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Wallace, Matthew L., Vincent Larivière, and Yves Gingras. "A Small World of Citations? The Influence of Collaboration Networks on Citation Practices." PLoS ONE 7, no. 3 (March 7, 2012): e33339. http://dx.doi.org/10.1371/journal.pone.0033339.

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Li, Jiang, and Peter Willett. "ArticleRank: a PageRank‐based alternative to numbers of citations for analysing citation networks." Aslib Proceedings 61, no. 6 (November 13, 2009): 605–18. http://dx.doi.org/10.1108/00012530911005544.

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Pichardo-Corpus, Juan A., J. Guillermo Contreras, and José A. de la Peña. "Parametric definition of the influence of a paper in a citation network using communicability functions." Journal of Complex Networks 7, no. 4 (January 16, 2019): 623–40. http://dx.doi.org/10.1093/comnet/cny037.

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Abstract Communicability functions quantify the flow of information between two nodes of a network. In this work, we use them to explore the concept of the influence of a paper in a citation network. These functions depend on a parameter. By varying the parameter in a continuous way we explore different definitions of influence. We study six citation networks, three from physics and three from computer science. As a benchmark, we compare our results against two frequently used measures: the number of citations of a paper and the PageRank algorithm. We show that the ranking of the articles in a network can be varied from being equivalent to the ranking obtained from the number of citations to a behaviour tending to the eigenvector centrality, these limits correspond to small and large values of the communicability-function parameter, respectively. At an intermediate value of the parameter a PageRank-like behaviour is recovered. As a test case, we apply communicability functions to two sets of articles, where at least one author of each paper was awarded a Nobel Prize for the research presented in the corresponding article.

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Butt, Bilal H., Muhammad Rafi, and Muhammad Sabih. "A systematic metadata harvesting workflow for analysing scientific networks." PeerJ Computer Science 7 (March 10, 2021): e421. http://dx.doi.org/10.7717/peerj-cs.421.

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One of the disciplines behind the science of science is the study of scientific networks. This work focuses on scientific networks as a social network having different nodes and connections. Nodes can be represented by authors, articles or journals while connections by citation, co-citation or co-authorship. One of the challenges in creating scientific networks is the lack of publicly available comprehensive data set. It limits the variety of analyses on the same set of nodes of different scientific networks. To supplement such analyses we have worked on publicly available citation metadata from Crossref and OpenCitatons. Using this data a workflow is developed to create scientific networks. Analysis of these networks gives insights into academic research and scholarship. Different techniques of social network analysis have been applied in the literature to study these networks. It includes centrality analysis, community detection, and clustering coefficient. We have used metadata of Scientometrics journal, as a case study, to present our workflow. We did a sample run of the proposed workflow to identify prominent authors using centrality analysis. This work is not a bibliometric study of any field rather it presents replicable Python scripts to perform network analysis. With an increase in the popularity of open access and open metadata, we hypothesise that this workflow shall provide an avenue for understanding scientific scholarship in multiple dimensions.

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Kreutz, Christin Katharina, Premtim Sahitaj, and Ralf Schenkel. "Evaluating semantometrics from computer science publications." Scientometrics 125, no. 3 (March 18, 2020): 2915–54. http://dx.doi.org/10.1007/s11192-020-03409-5.

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AbstractIdentification of important works and assessment of importance of publications in vast scientific corpora are challenging yet common tasks subjected by many research projects. While the influence of citations in finding seminal papers has been analysed thoroughly, citation-based approaches come with several problems. Their impracticality when confronted with new publications which did not yet receive any citations, area-dependent citation practices and different reasons for citing are only a few drawbacks of them. Methods relying on more than citations, for example semantic features such as words or topics contained in publications of citation networks, are regarded with less vigour while providing promising preliminary results. In this work we tackle the issue of classifying publications with their respective referenced and citing papers as either seminal, survey or uninfluential by utilising semantometrics. We use distance measures over words, semantics, topics and publication years of papers in their citation network to engineer features on which we predict the class of a publication. We present the SUSdblp dataset consisting of 1980 labelled entries to provide a means of evaluating this approach. A classification accuracy of up to .9247 was achieved when combining multiple types of features using semantometrics. This is +.1232 compared to the current state of the art (SOTA) which uses binary classification to identify papers from classes seminal and survey. The utilisation of one-vector representations for the ternary classification task resulted in an accuracy of .949 which is +.1475 compared to the binary SOTA. Classification based on information available at publication time derived with semantometrics resulted in an accuracy of .8152 while an accuracy of .9323 could be achieved when using one-vector representations.

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Deng, Rui. "Creating a New Combined Influence Measure: An Analysis of the Citation Network." Advanced Materials Research 989-994 (July 2014): 4789–93. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.4789.

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This paper utilizes social network analysis to create a comprehensive evolution system to determine the significance of research papers. Citation network was analyzed from H-index, Impact factor and relative citation rate, which indicates the influence of the author, published journal and the paper on the field respectively. Influence citation network was built visually though transforming the 16 science network paper’s citation multi-value matrix into two-value matrix in UCINET and by analyzing those influence measures, the scores of each paper were calculated and results maps through visualization. The results concern: the paper of Collective dynamics of `small-world' networks laid a foundation on network science. Besides, a general methodology is provided to determine the role or influence measure of academic journals.

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Jayasree, V., and M. D. Baby. "Scientometrics: Tools, Techniques and Software for Analysis." Indian Journal of Information Sources and Services 9, no. 2 (May 5, 2019): 116–21. http://dx.doi.org/10.51983/ijiss.2019.9.2.611.

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This paper aims to discuss the significance of e-resources on scientometrics study. Tools for scientometric analysis are listed out. Data collected from literature search and website of softwares. Citation tracking tools like Web of science, Scopus and Google Scholar citations, CiteseerX etc., are discussed. Various software tools for bibliometric analysis like Bibexcel, CiteSpace, Histcite, Pajek, Publish or Perish, Scholarometer, VOS viewer-tool for constructing and visualizing bibliometric networks, CitNet explorer – tool for visualizing and analysing citation networks of publications etc are discussed, The study concludes that combination of different software tools can be used for complete scientometric analysis and the familiarization of bibliometric software among students and researchers will help to promote research in scientometrics in a more productive method.

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Martinez-Perez, Clara, Cristina Alvarez-Peregrina, Cesar Villa-Collar, and Miguel Ángel Sánchez-Tena. "Citation Network Analysis of the Novel Coronavirus Disease 2019 (COVID-19)." International Journal of Environmental Research and Public Health 17, no. 20 (October 21, 2020): 7690. http://dx.doi.org/10.3390/ijerph17207690.

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Background: The first outbreaks of the new coronavirus disease, named COVID-19, occurred at the end of December 2019. This disease spread quickly around the world, with the United States, Brazil and Mexico being the countries the most severely affected. This study aims to analyze the relationship between different publications and their authors through citation networks, as well as to identify the research areas and determine which publication has been the most cited. Methods: The search for publications was carried out through the Web of Science database using terms such as “COVID-19” and “SARS-CoV-2” for the period between January and July 2020. The Citation Network Explorer software was used for publication analysis. Results: A total of 14,335 publications were found with 42,374 citations generated in the network, with June being the month with the largest number of publications. The most cited publication was “Clinical Characteristics of Coronavirus Disease 2019 in China” by Guan et al., published in April 2020. Nine groups comprising different research areas in this field, including clinical course, psychology, treatment and epidemiology, were found using the clustering functionality. Conclusions: The citation network offers an objective and comprehensive analysis of the main papers on COVID-19 and SARS-CoV-2.

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Sanchez-Tena, Miguel Angel, Cristina Alvarez-Peregrina, Jose Sanchez-Valverde, and Cesar Villa-Collar. "Current State and Future Trends: A Citation Network Analysis of the Orthokeratology Field." Journal of Ophthalmology 2019 (March 7, 2019): 1–6. http://dx.doi.org/10.1155/2019/6964043.

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Introduction. Citation network analysis is a powerful tool that allows for a visual and objective representation of the past, present, and potential future directions of a research field. The objective of this study is using citation analysis network to analyse the evolution of knowledge in the field of orthokeratology. Materials and Methods. The database used in this citation networks analysis study was Scopus. The descriptor used was “orthokeratology” limited to three fields: title, keywords, and/or abstract, analysing the five most cited authors. Only articles cited at least twenty times were used. The computer software used was UCINET with two types of analysis, qualitative and quantitative. Results. 27 nodes have been included according to the search and inclusion criteria. In qualitative analysis, based on illustrate results, the relationships among nodes and their positions and connections show how the study of Cho et al. in 2005 is clearly positioned as a central cutoff point in the network. Quantitative analysis reveals the normalized value of the sample and shows how the study of Cho et al. in 2005 presents the highest percentage of input connections. Conclusions. This study shows the state of the flow of information in the orthokeratology field by providing links in bibliographic citations from a qualitative and quantitative point of view.

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Rakoczy, Monika Ewa, Amel Bouzeghoub, Alda Lopes Gancarski, and Katarzyna Wegrzyn-Wolska. "Time-Dependent Influence Measurement in Citation Networks." Complex Systems Informatics and Modeling Quarterly, no. 17 (December 31, 2018): 24–43. http://dx.doi.org/10.7250/csimq.2018-17.02.

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FUJITA, Katsuhide, Yuya KAJIKAWA, Junichiro MORI, and Ichiro SAKATA. "Detecting Research Fronts Using Weighted Citation Networks." Joho Chishiki Gakkaishi 22, no. 2 (2012): 144–49. http://dx.doi.org/10.2964/jsik.22_144.

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Elango, B., Lutz Bornmann, and S. Shankar. "Study of citation networks in tribology research." Collnet Journal of Scientometrics and Information Management 10, no. 1 (January 2, 2016): 71–96. http://dx.doi.org/10.1080/09737766.2016.1177940.

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