Search (48 results, page 1 of 3)

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Abstract

The paper attempts to provide an alternative method for measuring the importance of scientific papers based on the Google's PageRank. The method is a meaningful extension of the common integer counting of citations and is then experimented for bringing PageRank to the citation analysis in a large citation network. It offers a more integrated picture of the publications' influence in a specific field. We firstly calculate the PageRanks of scientific papers. The distributional characteristics and comparison with the traditionally used number of citations are then analyzed in detail. Furthermore, the PageRank is implemented in the evaluation of research influence for several countries in the field of Biochemistry and Molecular Biology during the time period of 2000-2005. Finally, some advantages of bringing PageRank to the citation analysis are concluded.

Date

31. 7.2008 14:22:05

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Source

Library resources and technical services. 29(1985), S.189-194

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Abstract

Main path analysis is a popular method for extracting the scientific backbone from the citation network of a research domain. Existing approaches ignored the semantic relationships between the citing and cited publications, resulting in several adverse issues, in terms of coherence of main paths and coverage of significant studies. This paper advocated the semantic main path network analysis approach to alleviate these issues based on citation function analysis. A wide variety of SciBERT-based deep learning models were designed for identifying citation functions. Semantic citation networks were built by either including important citations, for example, extension, motivation, usage and similarity, or excluding incidental citations like background and future work. Semantic main path network was built by merging the top-K main paths extracted from various time slices of semantic citation network. In addition, a three-way framework was proposed for the quantitative evaluation of main path analysis results. Both qualitative and quantitative analysis on three research areas of computational linguistics demonstrated that, compared to semantics-agnostic counterparts, different types of semantic main path networks provide complementary views of scientific knowledge flows. Combining them together, we obtained a more precise and comprehensive picture of domain evolution and uncover more coherent development pathways between scientific ideas.

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Abstract

Indexes in general seek to provide a "key" to a body of literature intending to help the user in identifying, verifying, and/or locating individual or related items. The most common devices for collocation in indexes are authors' names and subjects. A different approach to collocating related items in an index is provided by a method called "citation indexing." Citation indexes attempt to link items through citations or references, in other works, by bringing together items cited in a particular work and the works citing a particular item. Citation indexing is based an the concept that there is a significant intellectual link between a document and each bibliographic item cited in it and that this link is useful to the scholar because an author's references to earlier writings identify relevant information to the subject of his current work. One of the major differences between the citation index and the traditional subject index is that the former, while listing current literature, also provides a retrospec tive view of past literature. While each issue of a traditional index is normally concerned only with the current literature, the citation index brings back retrospective literature in the form of cited references, thereby linking current scholarly works with earlier works. The advantages of the citation index have been considered to be its value as a tool for tracing the history of ideas or discoveries, for associating ideas between current and past work, and for evaluating works of individual authors or library collections. The concept of citation indexing is not new. It has been applied to legal literature since 1873 in a legal reference tool called Shepard's Citations. In the 1950s Eugene Garfield, a documentation consultant and founder and President of the Institute for Scientific Information (Philadelphia), developed the technique of citation indexing for scientific literature. This new application was facilitated by the availability of computer technology, resulting in a series of services: Science Citation Index (1955- ), Social Sciences Citation Index (1966- ), and the Arts & Humanities Index (1976- ). All three appear in printed versions and as machine-readable databases. In the following essay, the first in a series of articles and books elucidating the citation indexing system, Garfield traces the origin and beginning of this idea, its advantages, and the methods of preparing such indexes.

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Abstract

Purpose - The purpose of this paper is to report an investigation into the social and citation networks of three information scientists: David Nicholas, Peter Williams and Paul Huntington. Design/methodology/approach - Similarities between citation patterns and social closeness were identified and discussed. A total of 16 individuals in the citation network were identified and investigated using citation analysis, and a matrix formed of citations made between those in the network. Social connections between the 16 in the citation network were then investigated by means of a questionnaire, the results of which were merged into a separate matrix. These matrices were converted into visual social networks, using multidimensional scaling. A new deviance measure was devised for drawing comparisons between social and citation closeness in individual cases. Findings - Nicholas, Williams and Huntington were found to have cited 527 authors in the period 2000-2003, the 16 most cited becoming the subjects of further citation and social investigation. This comparison, along with the examination of visual representations indicates a positive correlation between social closeness and citation counts. Possible explanations for this correlation are discussed, and implications considered. Despite this correlation, the information scientists were found to cite widely outside their immediate social connections. Originality/value - Social network analysis has not been often used in combination with citation analysis to explore inter-relationships in research teams.

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Abstract

Citations support the communication of specialist knowledge by allowing authors and readers to make specific selections in several contexts at the same time. In the interactions between the social network of authors and the network of their reflexive communications, a sub textual code of communication with a distributed character has emerged. Citation analysis reflects on citation practices. Reference lists are aggregated in scientometric analysis using one of the available contexts to reduce the complexity: geometrical representations of dynamic operations are reflected in corresponding theories of citation. The specific contexts represented in the modern citation can be deconstructed from the perspective of the cultural evolution of scientific communication

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Abstract

In this article we demonstrate the use of an integrative approach to visualizing and tracking the development of scientific paradigms. This approach is designed to reveal the long-term process of competing scientific paradigms. We assume that a cluster of highly cited and cocited scientific publications in a cocitation network represents the core of a predominant scientific paradigm. The growth of a paradigm is depicted and animated through the rise of citation rates and the movement of its core cluster towards the center of the cocitation network. We study two cases of competing scientific paradigms in the real world: (1) the causes of mass extinctions, and (2) the connections between mad cow disease and a new variant of a brain disease in humans-vCJD. Various theoretical and practical issues concerning this approach are discussed.

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Abstract

The aggregated journal-journal citation matrix derived from Journal Citation Reports 2001 can be decomposed into a unique subject classification using the graph-analytical algorithm of bi-connected components. This technique was recently incorporated in software tools for social network analysis. The matrix can be assessed in terms of its decomposability using articulation points which indicate overlap between the components. The articulation points of this set did not exhibit a next-order network of "general science" journals. However, the clusters differ in size and in terms of the internal density of their relations. A full classification of the journals is provided in the Appendix. The clusters can also be extracted and mapped for the visualization.

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Abstract

Are computer science papers extended after they are published? We have surveyed 200 computer science publications, 100 journal articles, and 100 conference papers, using self-citations to identify potential and actual continuations. We are interested in determining the proportion of papers that do indeed continue, how and when the continuation takes place, and whether any distinctions are found between the journal and conference populations. Despite the implicit assumption of a research line behind each paper, manifest in the ubiquitous "future research" notes that close many of them, we find that more than 70% of the papers are never continued.

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Abstract

Traditional citation analysis has been widely applied to detect patterns of scientific collaboration, map the landscapes of scholarly disciplines, assess the impact of research outputs, and observe knowledge transfer across domains. It is, however, limited, as it assumes all citations are of similar value and weights each equally. Content-based citation analysis (CCA) addresses a citation's value by interpreting each one based on its context at both the syntactic and semantic levels. This paper provides a comprehensive overview of CAA research in terms of its theoretical foundations, methodical approaches, and example applications. In addition, we highlight how increased computational capabilities and publicly available full-text resources have opened this area of research to vast possibilities, which enable deeper citation analysis, more accurate citation prediction, and increased knowledge discovery.

Date

22. 8.2014 16:52:04

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Abstract

Domain visualization is one of the new research fronts resulted from the proliferation of information visualization, aiming to reveal the essence of a knowledge domain. Information visualization plays an integral role in modeling and representing intellectual structures associated with scientific disciplines. In this article, the domain of computer graphics is visualized based on author cocitation patterns derived from an 18-year span of the prestigious IEEE Computer Graphics and Applications (1982-1999). This domain visualization utilizes a series of visualization and animation techniques, including author cocitation maps, citation time lines, animation of a highdimensional specialty space, and institutional profiles. This approach not only augments traditional domain analysis and the understanding of scientific disciplines, but also produces a persistent and shared knowledge space for researchers to keep track the development of knowledge more effectively. The results of the domain visualization are discussed and triangulated in a broader context of the computer graphics field

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Abstract

Reports recent experiences with the publishing, via the Internet and WWW of ISI's biweekly newspaper, The Scientist; which was originally mounted on the NSFnet. Compares the use of the Internet for SDI by comparing Web searches via AltaVista with similar searches on CD-ROM. Predicts that future current awareness services and SDI services will be linked to electronic periodicals in electronic libraries. Concludes with a note on cited reference searching, a variation on the theme of hypertext searching, with particular reference to SCI and Web crawlers

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Abstract

The increasing availability of digital libraries with cross-citation data on the Internet enables new studies in bibliometrics. The paper focuses on the list of 10.000 top-cited authors in computer science available as part of CiteSeer. Using data from several consecutive lists a model of how authors accrue citations with time is constructed. By comparing the rate at which individual authors accrue citations with the average rate, predictions are made of how their ranking in the list will change in the future.

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Abstract

Using the Scopus dataset (1996-2007) a grand matrix of aggregated journal-journal citations was constructed. This matrix can be compared in terms of the network structures with the matrix contained in the Journal Citation Reports (JCR) of the Institute of Scientific Information (ISI). Because the Scopus database contains a larger number of journals and covers the humanities, one would expect richer maps. However, the matrix is in this case sparser than in the case of the ISI data. This is because of (a) the larger number of journals covered by Scopus and (b) the historical record of citations older than 10 years contained in the ISI database. When the data is highly structured, as in the case of large journals, the maps are comparable, although one may have to vary a threshold (because of the differences in densities). In the case of interdisciplinary journals and journals in the social sciences and humanities, the new database does not add a lot to what is possible with the ISI databases.

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Date

13. 7.2008 19:53:22