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Academic literature on the topic 'Citation networks'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 February 2022

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Journal articles on the topic "Citation networks"

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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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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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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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Dissertations / Theses on the topic "Citation networks"

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Leifeld, Philip. "Policy networks a citation analysis of the quantitative literature /." [S.l. : s.n.], 2007. http://nbn-resolving.de/urn:nbn:de:bsz:352-opus-26631.

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Dunaiski, Marcel Paul. "Analysing ranking algorithms and publication trends on scholarly citation networks." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/96106.

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Thesis (MSc)--Stellenbosch University, 2014.
ENGLISH ABSTRACT: Citation analysis is an important tool in the academic community. It can aid universities, funding bodies, and individual researchers to evaluate scientific work and direct resources appropriately. With the rapid growth of the scientific enterprise and the increase of online libraries that include citation analysis tools, the need for a systematic evaluation of these tools becomes more important. The research presented in this study deals with scientific research output, i.e., articles and citations, and how they can be used in bibliometrics to measure academic success. More specifically, this research analyses algorithms that rank academic entities such as articles, authors and journals to address the question of how well these algorithms can identify important and high-impact entities. A consistent mathematical formulation is developed on the basis of a categorisation of bibliometric measures such as the h-index, the Impact Factor for journals, and ranking algorithms based on Google’s PageRank. Furthermore, the theoretical properties of each algorithm are laid out. The ranking algorithms and bibliometric methods are computed on the Microsoft Academic Search citation database which contains 40 million papers and over 260 million citations that span across multiple academic disciplines. We evaluate the ranking algorithms by using a large test data set of papers and authors that won renowned prizes at numerous Computer Science conferences. The results show that using citation counts is, in general, the best ranking metric. However, for certain tasks, such as ranking important papers or identifying high-impact authors, algorithms based on PageRank perform better. As a secondary outcome of this research, publication trends across academic disciplines are analysed to show changes in publication behaviour over time and differences in publication patterns between disciplines.
AFRIKAANSE OPSOMMING: Sitasiesanalise is ’n belangrike instrument in die akademiese omgewing. Dit kan universiteite, befondsingsliggams en individuele navorsers help om wetenskaplike werk te evalueer en hulpbronne toepaslik toe te ken. Met die vinnige groei van wetenskaplike uitsette en die toename in aanlynbiblioteke wat sitasieanalise insluit, word die behoefte aan ’n sistematiese evaluering van hierdie gereedskap al hoe belangriker. Die navorsing in hierdie studie handel oor die uitsette van wetenskaplike navorsing, dit wil sê, artikels en sitasies, en hoe hulle gebruik kan word in bibliometriese studies om akademiese sukses te meet. Om meer spesifiek te wees, hierdie navorsing analiseer algoritmes wat akademiese entiteite soos artikels, outeers en journale gradeer. Dit wys hoe doeltreffend hierdie algoritmes belangrike en hoë-impak entiteite kan identifiseer. ’n Breedvoerige wiskundige formulering word ontwikkel uit ’n versameling van bibliometriese metodes soos byvoorbeeld die h-indeks, die Impak Faktor vir journaale en die rang-algoritmes gebaseer op Google se PageRank. Verder word die teoretiese eienskappe van elke algoritme uitgelê. Die rang-algoritmes en bibliometriese metodes gebruik die sitasiedatabasis van Microsoft Academic Search vir berekeninge. Dit bevat 40 miljoen artikels en meer as 260 miljoen sitasies, wat oor verskeie akademiese dissiplines strek. Ons gebruik ’n groot stel toetsdata van dokumente en outeers wat bekende pryse op talle rekenaarwetenskaplike konferensies gewen het om die rang-algoritmes te evalueer. Die resultate toon dat die gebruik van sitasietellings, in die algemeen, die beste rangmetode is. Vir sekere take, soos die gradeering van belangrike artikels, of die identifisering van hoë-impak outeers, presteer algoritmes wat op PageRank gebaseer is egter beter. ’n Sekondêre resultaat van hierdie navorsing is die ontleding van publikasie tendense in verskeie akademiese dissiplines om sodoende veranderinge in publikasie gedrag oor tyd aan te toon en ook die verskille in publikasie patrone uit verskillende dissiplines uit te wys.

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Glötzl, Florentin, and Ernest Aigner. "Orthodox Core-Heterodox Periphery? Contrasting Citation Networks of Economics Departments in Vienna." Taylor & Francis, 2018. http://epub.wu.ac.at/6631/1/09538259.2018.pdf.

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The notion of an "orthodox core-heterodox periphery" structure and the extent of interdisciplinary links have been widely discussed, and partially investigated bibliometrically, within economic discourse. We extend this research by applying tools from social network analysis to citation data of three economics departments located in Vienna, two mainstream and one non-mainstream, to assess their relative citation patterns. We show that both mainstream economics departments follow the asserted core-periphery pattern and have a mono-disciplinary research focus, while the citation network of the non-mainstream department has a polycentric structure and is both more heterodox and interdisciplinary. These findings suggest that discussions about the future of heterodox economics should pay more attention to the organizational level and seek allies from other disciplines.

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Meiklejohn, Luke S. "How to attribute credit if you must." Master's thesis, Faculty of Commerce, 2021. http://hdl.handle.net/11427/33802.

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Data ownership is of fundamental importance in the digital economy of today. Commercializing academic research, whilst maintaining ownership of it, is a task that can now be accomplished due to the strengths of blockchain technology, which allows data to be registered, made unique, and traced to its origins. We propose a blockchain use-case for licencing academic research, based off an academic project named UniCoin. In this thesis, we discuss how to fairly attribute credit between all sources of knowledge that contribute to new pieces of academic research, using citation network analysis and centrality measures. Katz centrality, in-degree centrality, and PageRank are three potentially useful centrality measures, with varying results: these are compared using case studies based on three papers co-authored by Andrei Shleifer. We use these centrality measures to guide how to fairly attribute credit, and thus how to distribute licencing revenues generated through UniCoin.

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AbuRa'ed, Ahmed Ghassan Tawfiq. "Automatic generation of descriptive related work reports." Doctoral thesis, Universitat Pompeu Fabra, 2020. http://hdl.handle.net/10803/669975.

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A related work report is a section in a research paper which integrates key information from a list of related scientific papers providing context to the work being presented. Related work reports can either be descriptive or integrative. Integrative related work reports provide a high-level overview and critique of the scientific papers by comparing them with each other, providing fewer details of individual studies. Descriptive related work reports, instead, provide more in-depth information about each mentioned study providing information such as methods and results of the cited works. In order to write a related work report, scientist have to identify, condense/summarize, and combine relevant information from different scientific papers. However, such task is complicated due to the available volume of scientific papers. In this context, the automatic generation of related work reports appears to be an important problem to tackle. The automatic generation of related work reports can be considered as an instance of the multi-document summarization problem where, given a list of scientific papers, the main objective is to automatically summarize those scientific papers and generate related work reports. In order to study the problem of related work generation, we have developed a manually annotated, machine readable data-set of related work sections, cited papers (e.g. references) and sentences, together with an additional layer of papers citing the references. We have also investigated the relation between a citation context in a citing paper and the scientific paper it is citing so as to properly model cross-document relations and inform our summarization approach. Moreover, we have also investigated the identification of explicit and implicit citations to a given scientific paper which is an important task in several scientific text mining activities such as citation purpose identification, scientific opinion mining, and scientific summarization. We present both extractive and abstractive methods to summarize a list of scientific papers by utilizing their citation network. The extractive approach follows three stages: scoring the sentences of the scientific papers based on their citation network, selecting sentences from each scientific paper to be mentioned in the related work report, and generating an organized related work report by grouping the sentences of the scientific papers that belong to the same topic together. On the other hand, the abstractive approach attempts to generate citation sentences to be included in a related work report, taking advantage of current sequence-to-sequence neural architectures and resources that we have created specifically for this task. The thesis also presents and discusses automatic and manual evaluation of the generated related work reports showing the viability of the proposed approaches.
La sección de trabajos relacionados de un artículo científico resume e integra información clave de una lista de documentos científicos relacionados con el trabajo que se presenta. Para redactar esta sección del artículo científico el autor debe identificar, condensar/resumir y combinar información relevante de diferentes artículos. Esta tarea es complicada debido al gran volumen disponible de artículos científicos. En este contexto, la generación automática de tales secciones es un problema importante a abordar. La generación automática de secciones de trabajo relacionados puede ser considerada como una instancia del problema de resumen de documentos múltiples donde, dada una lista de documentos científicos, el objetivo es resumir automáticamente esos documentos científicos y generar la sección de trabajos relacionados. Para estudiar este problema, hemos creado un corpus de secciones de trabajos relacionados anotado manualmente y procesado automáticamente. Asimismo, hemos investigado la relación entre las citaciones y el artículo científico que se cita para modelar adecuadamente las relaciones entre documentos y, así, informar nuestro método de resumen automático. Además, hemos investigado la identificación de citaciones implícitas a un artículo científico dado que es una tarea importante en varias actividades de minería de textos científicos. Presentamos métodos extractivos y abstractivos para resumir una lista de artículos científicos utilizando su red de citaciones. El enfoque extractivo sigue tres etapas: cálculo de la relevancia las oraciones de cada artículo en función de la red de citaciones, selección de oraciones de cada artículo científico para integrarlas en el resumen y generación de la sección de trabajos relacionados agrupando las oraciones por tema. Por otro lado, el enfoque abstractivo intenta generar citaciones para incluirlas en un resumen utilizando redes neuronales y recursos que hemos creado específicamente para esta tarea. La tesis también presenta y discute la evaluación automática y manual de los resúmenes generados automáticamente, demostrando la viabilidad de los enfoques propuestos.
Una secció d’antecedents o estat de l’art d’un articulo científic resumeix la informació clau d'una llista de documents científics relacionats amb el treball que es presenta. Per a redactar aquesta secció de l’article científic l’autor ha d’identificar, condensar / resumir i combinar informació rellevant de diferents articles. Aquesta activitat és complicada per causa del gran volum disponible d’articles científics. En aquest context, la generació automàtica d’aquestes seccions és un problema important a abordar. La generació automàtica d’antecedents o d’estat de l’art pot considerar-se com una instància del problema de resum de documents. Per estudiar aquest problema, es va crear un corpus de seccions d’estat de l’art d’articles científics manualment anotat i processat automàticament. Així mateix, es va investigar la relació entre citacions i l’article científic que es cita per modelar adequadament les relacions entre documents i, així, informar el nostre mètode de resum automàtic. A més, es va investigar la identificació de citacions implícites a un article científic que és un problema important en diverses activitats de mineria de textos científics. Presentem mètodes extractius i abstractius per resumir una llista d'articles científics utilitzant el conjunt de citacions de cada article. L’enfoc extractiu segueix tres etapes: càlcul de la rellevància de les oracions de cada article en funció de les seves citacions, selecció d’oracions de cada article científic per a integrar-les en el resum i generació de la secció de treballs relacionats agrupant les oracions per tema. Per un altre costat, l’enfoc abstractiu implementa la generació de citacions per a incloure-les en un resum que utilitza xarxes neuronals i recursos que hem creat específicament per a aquest tasca. La tesi també presenta i discuteix l'avaluació automàtica i el manual dels resums generats automàticament, demostrant la viabilitat dels mètodes proposats.

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Alshareef, Abdulrhman M. "Academic Recommendation System Based on the Similarity Learning of the Citation Network Using Citation Impact." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39111.

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In today's significant and rapidly increasing amount of scientific publications, exploring recent studies in a given research area and building an effective scientific collaboration has become more challenging than any time before. Scientific production growth has been increasing the difficulties for identifying the most relevant papers to cite or to find an appropriate conference or journal to submit a paper to publish. As a result, authors and publishers rely on different analytical approaches in order to measure the relationship among the citation network. Different parameters have been used such as the impact factor, number of citations, co-citation to assess the impact of the produced research publication. However, using one assessing factor considers only one level of relationship exploration, since it does not reflect the effect of the other factors. In this thesis, we propose an approach to measure the Academic Citation Impact that will help to identify the impact of articles, authors, and venues at their extended nearby citation network. We combine the content similarity with the bibliometric indices to evaluate the citation impact of articles, authors, and venues in their surrounding citation network. Using the article metadata, we calculate the semantic similarity between any two articles in the extended network. Then we use the similarity score and bibliometric indices to evaluate the impact of the articles, authors, and venues among their extended nearby citation network. Furthermore, we propose an academic recommendation model to identify the latent preferences among the citation network of the given article in order to expose the concealed connection between the academic objects (articles, authors, and venues) at the citation network of the given article. To reveal the degree of trust for collaboration between academic objects (articles, authors, and venues), we use the similarity learning to estimate the collaborative confidence score that represents the anticipation of a prospect relationship between the academic objects among a scientific community. We conducted an offline experiment to measure the accuracy of delivering personalized recommendations, based on the user’s selection preferences; real-world datasets were used. Our evaluation results show a potential improvement to the quality of the recommendation when compared to baseline recommendation algorithms that consider co-citation information.

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Alfraidi, Hanadi Humoud A. "Interactive System for Scientific Publication Visualization and Similarity Measurement based on Citation Network." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/33135.

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Online scientific publications are becoming more and more popular. The number of publications we can access almost instantaneously is rapidly increasing. This makes it more challenging for researchers to pursue a topic, review literature, track research history or follow research trends. Using online resources such as search engines and digital libraries is helpful to find scientific publications, however most of the time the user ends up with an overwhelming amount of linear results to go through. This thesis proposes an alternative system, which takes advantage of citation/reference relations between publications. This demonstrates better insight of the hierarchy distribution of publications around a given topic. We also utilize information visualization techniques to represent the publications as a network. Our system is designed to automatically retrieve publications from Google Scholar and visualize them as a 2-dimensional graph representation using the citation relations. In this, the nodes represent the documents while the links represent the citation/reference relations between them. Our visualization system provides a better view of publications, making it easier to identify the research flow, connect publications, and assess similarities/differences between them. It is an interactive web based system, which allows the users to get more information about any selected publication and calculate a similarity score between two selected publications. Traditionally, similar documents are found using Natural Language Processing (NLP), which compares documents based on matching their contents. In the proposed method, similar documents are found using the citation/reference relations which are iii represented by the relationship that was originally inputted by the authors. We propose a new path based metric for measuring the similarity scores between any pair of publications. This is based on both the number of paths and the length of each path. More paths and shorter lengths increase the similarity score. We compare our similarity score results with another similarity score from Scurtu’s Document Similarity [1] that uses the NLP method. We then use the average of the similarity scores collected from 15 users as a ground truth to validate the efficiency of our method. The results indicate that our Citation Network approach yielded better scores than Scurtu’s approach.

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Maier, Gunther, Alexander Kaufmann, and Michael Vyborny. "Is regional science a scientific discipline? Answers from a citation based Social Network Analysis." Institut für Regional- und Umweltwirtschaft, WU Vienna University of Economics and Business, 2008. http://epub.wu.ac.at/1226/1/document.pdf.

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From its very beginnings, regional science has been open to intellectual exchange with many other scientific disciplines. This has led to cross-fertilization, but also to problems concerning the intellectual identity of regional science. After half a century of history of the field, it is time to ask the question, whether or not regional science has developed into a scientific discipline in these decades. In this paper we use cross-citation data between 464 journals in different disciplines to answer this question. With this data set we attempt to find out, how strongly regional science journals are interconnected by citations as compared to their citation links to journals in neighbouring disciplines. We find that when we consider the raw citation data, regional science becomes fragmented with its journals tied to those from economics, geography, planning, etc. When we standardize the citation information to take into account size differences between journals, however, regional science appears to form a strong and well connected dscientific discipline.
Series: SRE - Discussion Papers

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Runelöv, Martin. "Finding seminal scientific publications with graph mining." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-172382.

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We investigate the applicability of network analysis to the problem of finding seminal publications in scientific publishing. In particular, we focus on the network measures betweenness centrality, the so-called backbone graph, and the burstiness of citations. The metrics are evaluated using precision-related scores with respect to gold standards based on fellow programmes and manual annotation. Citation counts, PageRank, and random selection are used as baselines. We find that the backbone graph provides us with a way to possibly discover seminal publications with low citation count, and combining betweenness and burstiness gives results on par with citation count.
I detta examensarbete undersöks det huruvida analys av citeringsgrafer kan användas för att finna betydelsefulla vetenskapliga publikationer. Framför allt studeras ”betweenness”-centralitet, den så kallade ”backbone”-grafen samt ”burstiness” av citeringar. Dessa mått utvärderas med hjälp av precisionsmått med avseende på guldstandarder baserade på ’fellow’-program samt via manuell annotering. Antal citeringar, PageRank, och slumpmässigt urval används som jämförelse. Resultaten visar att ”backbone”-grafen kan bidra till att eventuellt upptäcka betydelsefulla publikationer med ett lågt antal citeringar samt att en kombination av ”betweenness” och ”burstiness” ger resultat i nivå med de man får av att räkna antal citeringar.

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Glötzl, Florentin, and Ernest Aigner. "Pluralism in the Market of Science? A citation network analysis of economic research at universities in Vienna." WU Vienna University of Economics and Business, 2015. http://epub.wu.ac.at/4730/1/EcolEcon_WorkingPaper_2015_5.pdf.

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Pluralism has become a central issue not only in the public discourse but also in heterodox economics, as the focus on impact factors and rankings based on citations continues to increase. This marketization of science has been an institutional vehicle for the economic mainstream to promote its ideas. Citations thus have become a central currency in economics as a discipline. At the same time they allow to investigate patterns in the discourse. Analyzing articles published by the two major economics departments and the more interdisciplinary Department for Socioeconomics in Vienna, this paper is novel in applying both bibliometric techniques and citation network analysis on the department level. We find that (1) Articles in heterodox journals strongly reference the economic mainstream, while the mainstream does not cite heterodox journals, (2) Articles written by researchers of the Department of Socioeconomics cite more heterodox journals irrespective of whether they are published in mainstream or heterodox journals, (3) The economics departments display a citation network exhibiting a clear "mainstream core - heterodox periphery" structure, as Dobusch & Kapeller (2012b) suggest the overall discourse in economics to be, while the Department of Socioeconomics could be described as a plural though not pluralistic department with many distinct modules in the network , reflecting various disciplines, topics and schools of thought. (authors' abstract)
Series: Ecological Economic Papers

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Books on the topic "Citation networks"

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Golosovsky, Michael. Citation Analysis and Dynamics of Citation Networks. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28169-4.

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Harnack, Andrew. Online!: Citation styles. New York, NY: Bedford/St. Martin's, 2000.

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Hacker, Diana. Research and documentation online. [New York, NY?]: Bedford/St. Martin's, 1998.

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Kyle, Noeline. Citing historical sources: A manual for family historians. St Agnes, SA: Unlock the Past, 2013.

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Brian, Kelly. iSearch: Psychology. Boston, MA: Allyn and Bacon, 2003.

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La recherche en fiscalité canadienne. 3rd ed. Scarborough, Ont: Carswell, 2011.

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B, Barnes Susan, and Barr Linda R, eds. Web research: Selecting, evaluating, and citing. Boston: Pearson/Allyn & Bacon, 2006.

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Radford, Marie L. Web research: Selecting, evaluating, and citing. Boston: Allyn and Bacon, 2002.

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Golosovsky, Michael. Citation Analysis and Dynamics of Citation Networks. Springer, 2019.

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Transient hypergraphs for citation networks. Ottawa: National Library of Canada, 1989.

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Book chapters on the topic "Citation networks"

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Radicchi, Filippo, Santo Fortunato, and Alessandro Vespignani. "Citation Networks." In Understanding Complex Systems, 233–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23068-4_7.

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Csárdi, Gábor. "Dynamics of Citation Networks." In Artificial Neural Networks – ICANN 2006, 698–709. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11840817_73.

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Peroni, Silvio, David Shotton, and Fabio Vitali. "Building Citation Networks with SPACIN." In Lecture Notes in Computer Science, 162–66. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58694-6_23.

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Renoust, Benjamin, Vivek Claver, and Jean-François Baffier. "Flows of Knowledge in Citation Networks." In Studies in Computational Intelligence, 159–70. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50901-3_13.

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Kralj, Jan, Anita Valmarska, Marko Robnik-Šikonja, and Nada Lavrač. "Mining Text Enriched Heterogeneous Citation Networks." In Advances in Knowledge Discovery and Data Mining, 672–83. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18038-0_52.

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Mathur, Sandeep, and Loveleen Gaur. "Predictability, Power and Procedures of Citation Analysis." In Lecture Notes in Networks and Systems, 51–59. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9689-6_6.

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Zhang, Bo, Tiezheng Nie, Derong Shen, Yue Kou, Ge Yu, and Ziwei Zhou. "A Graph Clustering Algorithm for Citation Networks." In Web Technologies and Applications, 414–18. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45817-5_37.

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Yin, Jun, and Xiaoming Li. "Personalized Citation Recommendation via Convolutional Neural Networks." In Web and Big Data, 285–93. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63564-4_23.

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Waltman, Ludo, and Erjia Yan. "PageRank-Related Methods for Analyzing Citation Networks." In Measuring Scholarly Impact, 83–100. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10377-8_4.

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Fushimi, Takayasu, Tetsuji Satoh, and Noriko Kando. "Dynamic Visualization of Citation Networks and Detection of Influential Node Addition." In Complex Networks IX, 291–302. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73198-8_25.

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Conference papers on the topic "Citation networks"

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Ji, Taoran, Zhiqian Chen, Nathan Self, Kaiqun Fu, Chang-Tien Lu, and Naren Ramakrishnan. "Patent Citation Dynamics Modeling via Multi-Attention Recurrent Networks." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/364.

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Modeling and forecasting forward citations to a patent is a central task for the discovery of emerging technologies and for measuring the pulse of inventive progress. Conventional methods for forecasting these forward citations cast the problem as analysis of temporal point processes which rely on the conditional intensity of previously received citations. Recent approaches model the conditional intensity as a chain of recurrent neural networks to capture memory dependency in hopes of reducing the restrictions of the parametric form of the intensity function. For the problem of patent citations, we observe that forecasting a patent's chain of citations benefits from not only the patent's history itself but also from the historical citations of assignees and inventors associated with that patent. In this paper, we propose a sequence-to-sequence model which employs an attention-of-attention mechanism to capture the dependencies of these multiple time sequences. Furthermore, the proposed model is able to forecast both the timestamp and the category of a patent's next citation. Extensive experiments on a large patent citation dataset collected from USPTO demonstrate that the proposed model outperforms state-of-the-art models at forward citation forecasting.

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"Citation information." In Networks (DSN). IEEE, 2010. http://dx.doi.org/10.1109/dsn.2010.5544401.

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"Citation information." In Networks (DSN). IEEE, 2011. http://dx.doi.org/10.1109/dsn.2011.5958199.

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"Citation information." In 2010 International Conference on Dependable Systems and Networks Workshops (DSN-W). IEEE, 2010. http://dx.doi.org/10.1109/dsnw.2010.5542632.

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"Citation information." In 2008 IEEE International Conference on Dependable Systems and Networks With FTCS and DCC (DSN). IEEE, 2008. http://dx.doi.org/10.1109/dsn.2008.4630062.

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"Citation information." In 2011 IEEE/IFIP 41st International Conference on Dependable Systems and Networks Workshops (DSN-W). IEEE, 2011. http://dx.doi.org/10.1109/dsnw.2011.5958789.

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Liu, Tianpeng, and Kan Li. "A citation similarity based community detection method in citation networks." In 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE, 2015. http://dx.doi.org/10.1109/iaeac.2015.7428536.

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Guo, Zhen, Zhongfei Zhang, Shenghuo Zhu, Yun Chi, and Yihong Gong. "Knowledge Discovery from Citation Networks." In 2009 Ninth IEEE International Conference on Data Mining (ICDM). IEEE, 2009. http://dx.doi.org/10.1109/icdm.2009.137.

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Valverde, Sergi. "Evolution of patent citation networks." In 2014 Complexity in Engineering (COMPENG). IEEE, 2014. http://dx.doi.org/10.1109/compeng.2014.6994688.

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Mahdabi, Parvaz, and Fabio Crestani. "Query-Driven Mining of Citation Networks for Patent Citation Retrieval and Recommendation." In CIKM '14: 2014 ACM Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2661829.2661899.

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Reports on the topic "Citation networks"

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McLane, V. Citation guidelines for nuclear data retrieved from databases resident at the Nuclear Data Centers Network. Office of Scientific and Technical Information (OSTI), July 1996. http://dx.doi.org/10.2172/380333.

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Hilbrecht, Margo, David Baxter, Alexander V. Graham, and Maha Sohail. Research Expertise and the Framework of Harms: Social Network Analysis, Phase One. GREO, December 2020. http://dx.doi.org/10.33684/2020.006.

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In 2019, the Gambling Commission announced a National Strategy to Reduce Gambling Harms. Underlying the strategy is the Framework of Harms, outlined in Measuring gambling-related harms: A framework for action. "The Framework" adopts a public health approach to address gambling-related harm in Great Britain across multiple levels of measurement. It comprises three primary factors and nine related subfactors. To advance the National Strategy, all componentsneed to be supported by a strong evidence base. This report examines existing research expertise relevant to the Framework amongacademics based in the UK. The aim is to understand the extent to which the Framework factors and subfactors have been studied in order to identify gaps in expertise and provide evidence for decision making thatisrelevant to gambling harms research priorities. A social network analysis identified coauthor networks and alignment of research output with the Framework. The search strategy was limited to peer-reviewed items and covered the 12-year period from 2008 to 2019. Articles were selected using a Web of Science search. Of the 1417 records identified in the search, the dataset was refined to include only those articles that could be assigned to at least one Framework factor (n = 279). The primary factors and subfactors are: Resources:Work and Employment, Money and Debt, Crime;Relationships:Partners, Families and Friends, Community; and Health:Physical Health, Psychological Distress, and Mental Health. We used Gephi software to create visualisations reflecting degree centrality (number of coauthor networks) so that each factor and subfactor could be assessed for the density of research expertise and patterns of collaboration among coauthors. The findings show considerable variation by framework factor in the number of authors and collaborations, suggesting a need to develop additional research capacity to address under-researched areas. The Health factor subcategory of Mental Health comprised almost three-quarters of all citations, with the Resources factor subcategory of Money and Debt a distant second at 12% of all articles. The Relationships factor, comprised of two subfactors, accounted for less than 10%of total articles. Network density varied too. Although there were few collaborative networks in subfactors such as Community or Work and Employment, all Health subfactors showed strong levels of collaboration. Further, some subfactors with a limited number of researchers such as Partners, Families, and Friends and Money and debt had several active collaborations. Some researchers’ had publications that spanned multiple Framework factors. These multiple-factor researchers usually had a wide range of coauthors when compared to those who specialised (with the exception of Mental Health).Others’ collaborations spanned subfactors within a factor area. This was especially notable forHealth. The visualisations suggest that gambling harms research expertise in the UK has considerable room to grow in order to supporta more comprehensive, locally contextualised evidence base for the Framework. To do so, priority harms and funding opportunities will need further consideration. This will require multi-sector and multidisciplinary collaboration consistent with the public health approach underlying the Framework. Future research related to the present analysis will explore the geographic distribution of research activity within the UK, and research collaborations with harms experts internationally.

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