Search (53 results, page 1 of 3)

  • × language_ss:"e"
  • × theme_ss:"Informetrie"
  • × year_i:[2000 TO 2010}
  1. Shibata, N.; Kajikawa, Y.; Takeda, Y.; Matsushima, K.: Comparative study on methods of detecting research fronts using different types of citation (2009) 0.07 
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    Abstract
    In this article, we performed a comparative study to investigate the performance of methods for detecting emerging research fronts. Three types of citation network, co-citation, bibliographic coupling, and direct citation, were tested in three research domains, gallium nitride (GaN), complex network (CNW), and carbon nanotube (CNT). Three types of citation network were constructed for each research domain, and the papers in those domains were divided into clusters to detect the research front. We evaluated the performance of each type of citation network in detecting a research front by using the following measures of papers in the cluster: visibility, measured by normalized cluster size, speed, measured by average publication year, and topological relevance, measured by density. Direct citation, which could detect large and young emerging clusters earlier, shows the best performance in detecting a research front, and co-citation shows the worst. Additionally, in direct citation networks, the clustering coefficient was the largest, which suggests that the content similarity of papers connected by direct citations is the greatest and that direct citation networks have the least risk of missing emerging research domains because core papers are included in the largest component.
    Date
    22. 3.2009 17:52:50
  2. Zhang, Y.; Jansen, B.J.; Spink, A.: Identification of factors predicting clickthrough in Web searching using neural network analysis (2009) 0.07 
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    Abstract
    In this research, we aim to identify factors that significantly affect the clickthrough of Web searchers. Our underlying goal is determine more efficient methods to optimize the clickthrough rate. We devise a clickthrough metric for measuring customer satisfaction of search engine results using the number of links visited, number of queries a user submits, and rank of clicked links. We use a neural network to detect the significant influence of searching characteristics on future user clickthrough. Our results show that high occurrences of query reformulation, lengthy searching duration, longer query length, and the higher ranking of prior clicked links correlate positively with future clickthrough. We provide recommendations for leveraging these findings for improving the performance of search engine retrieval and result ranking, along with implications for search engine marketing.
    Date
    22. 3.2009 17:49:11
  3. Chen, C.: CiteSpace II : detecting and visualizing emerging trends and transient patterns in scientific literature (2006) 0.06 
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    Abstract
    This article describes the latest development of a generic approach to detecting and visualizing emerging trends and transient patterns in scientific literature. The work makes substantial theoretical and methodological contributions to progressive knowledge domain visualization. A specialty is conceptualized and visualized as a time-variant duality between two fundamental concepts in information science: research fronts and intellectual bases. A research front is defined as an emergent and transient grouping of concepts and underlying research issues. The intellectual base of a research front is its citation and co-citation footprint in scientific literature - an evolving network of scientific publications cited by research-front concepts. Kleinberg's (2002) burst-detection algorithm is adapted to identify emergent research-front concepts. Freeman's (1979) betweenness centrality metric is used to highlight potential pivotal points of paradigm shift over time. Two complementary visualization views are designed and implemented: cluster views and time-zone views. The contributions of the approach are that (a) the nature of an intellectual base is algorithmically and temporally identified by emergent research-front terms, (b) the value of a co-citation cluster is explicitly interpreted in terms of research-front concepts, and (c) visually prominent and algorithmically detected pivotal points substantially reduce the complexity of a visualized network. The modeling and visualization process is implemented in CiteSpace II, a Java application, and applied to the analysis of two research fields: mass extinction (1981-2004) and terrorism (1990-2003). Prominent trends and pivotal points in visualized networks were verified in collaboration with domain experts, who are the authors of pivotal-point articles. Practical implications of the work are discussed. A number of challenges and opportunities for future studies are identified.
    Date
    22. 7.2006 16:11:05
  4. Yan, E.; Ding, Y.: Applying centrality measures to impact analysis : a coauthorship network analysis (2009) 0.05 
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    Abstract
    Many studies on coauthorship networks focus on network topology and network statistical mechanics. This article takes a different approach by studying micro-level network properties with the aim of applying centrality measures to impact analysis. Using coauthorship data from 16 journals in the field of library and information science (LIS) with a time span of 20 years (1988-2007), we construct an evolving coauthorship network and calculate four centrality measures (closeness centrality, betweenness centrality, degree centrality, and PageRank) for authors in this network. We find that the four centrality measures are significantly correlated with citation counts. We also discuss the usability of centrality measures in author ranking and suggest that centrality measures can be useful indicators for impact analysis.
  5. Park, H.W.; Barnett, G.A.; Nam, I.-Y.: Hyperlink - affiliation network structure of top Web sites : examining affiliates with hyperlink in Korea (2002) 0.04 
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    Abstract
    This article argues that individual Web sites form hyperlink-affiliations with others for the purpose of strengthening their individual trust, expertness, and safety. It describes the hyperlink-affiliation network structure of Korea's top 152 Web sites. The data were obtained from their Web sites for October 2000. The results indicate that financial Web sites, such as credit card and stock Web sites, occupy the most central position in the network. A cluster analysis reveals that the structure of the hyperlink-affiliation network is influenced by the financial Web sites with which others are affiliated. These findings are discussed from the perspective of Web site credibility.
  6. Liu, Z.; Wang, C.: Mapping interdisciplinarity in demography : a journal network analysis (2005) 0.04 
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  7. Larsen, B.: Exploiting citation overlaps for information retrieval : generating a boomerang effect from the network of scientific papers (2002) 0.03 
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  8. Leydesdorff, L.; Heimeriks, G.: ¬The self-organization of the European information society : the case of "biotechnology" (2001) 0.03 
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    Abstract
    Fields of technoscience like biotechnology develop in a network mode: disciplinary insights from different backgrounds are recombined as competing innovation systems are continuously reshaped. The ongoing process of integration at the European level generates an additional network of transnational collaborations. Using the title words of scientific publications in five core journals of biotechnology, multivariate analysis is used to distinguish between the intellectual organization of the publications in terms of title words and the institutional network in terms of addresses of documents. The interaction among the representation of intellectual space in terms of words and co-words, and the potentially European network system is compared with the document sets with American and Japanese addresses. The European system can also be decomposed in terms of the contributions of member states. Whereas a European vocabulary can be made visible at the global level, this communality disappears by this decomposition. The network effect at the European level can be considered as institutional more than cognitive
  9. Ding, Y.; Yan, E.; Frazho, A.; Caverlee, J.: PageRank for ranking authors in co-citation networks (2009) 0.03 
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    Abstract
    This paper studies how varied damping factors in the PageRank algorithm influence the ranking of authors and proposes weighted PageRank algorithms. We selected the 108 most highly cited authors in the information retrieval (IR) area from the 1970s to 2008 to form the author co-citation network. We calculated the ranks of these 108 authors based on PageRank with the damping factor ranging from 0.05 to 0.95. In order to test the relationship between different measures, we compared PageRank and weighted PageRank results with the citation ranking, h-index, and centrality measures. We found that in our author co-citation network, citation rank is highly correlated with PageRank with different damping factors and also with different weighted PageRank algorithms; citation rank and PageRank are not significantly correlated with centrality measures; and h-index rank does not significantly correlate with centrality measures but does significantly correlate with other measures. The key factors that have impact on the PageRank of authors in the author co-citation network are being co-cited with important authors.
  10. Johnson, B.; Oppenheim, C.: How socially connected are citers to those that they cite? (2007) 0.03 
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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.
  11. Nicolaisen, J.: Citation analysis (2007) 0.03 
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    Date
    13. 7.2008 19:53:22
  12. Van der Veer Martens, B.: Do citation systems represent theories of truth? (2001) 0.02 
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    Date
    22. 7.2006 15:22:28
  13. Kim, P.J.; Lee, J.Y.; Park, J.-H.: Developing a new collection-evaluation method : mapping and the user-side h-index (2009) 0.02 
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    Abstract
    This study proposes a new visualization method and index for collection evaluation. Specifically, it develops a network-based mapping technique and a user-focused Hirsch index (user-side h-index) given the lack of previous studies on collection evaluation methods that have used the h-index. A user-side h-index is developed and compared with previous indices (use factor, difference of percentages, collection-side h-index) that represent the strengths of the subject classes of a library collection. The mapping procedure includes the subject-usage profiling of 63 subject classes and collection-usage map generations through the pathfinder network algorithm. Cluster analyses are then conducted upon the pathfinder network to generate 5 large and 14 small clusters. The nodes represent the strengths of the subject-class usages reflected by the user-side h-index. The user-side h-index was found to have advantages (e.g., better demonstrating the real utility of each subject class) over the other indices. It also can more clearly distinguish the strengths between the subject classes than can collection-side h-index. These results may help to identify actual usage and strengths of subject classes in library collections through visualized maps. This may be a useful rationale for the establishment of the collection-development plan.
  14. Chen, C.; Cribbin, T.; Macredie, R.; Morar, S.: Visualizing and tracking the growth of competing paradigms : two case studies (2002) 0.02 
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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.
  15. Leydesdorff, L.: Clusters and maps of science journals based on bi-connected graphs in Journal Citation Reports (2004) 0.02 
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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.
  16. Perry, C.A.: Network influences on scholarly communication in developmental dyslexia : a longitudinal follow-up (2003) 0.02 
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    Abstract
    Perry collects co-citation data for the years 1994 to 1998 on 74 Developmental Dyslexia researchers whose co-citation patterns and personally reported interactions she originally studied form 1976 to 1993. The original study indicated discrepancies between sociometric and bibliometric networks of interaction, delays in the emergence of new perspectives and the possibility of the convergence of perspectives facilitated by central researchers. Mapping for the present study was done by multi-dimensional scaling rather than the principle components factor analysis in the earlier study, but both clustering techniques and factor analysis were applied to the new data. Researchers with phonological and with neuroscience perspectives area associated with different co-citation patterns. Research groups grow more distinct over time with the neuroscience-vision subgroup increasing in density, but other sub-groups showing some tendency toward integration. The personal networks differences with the co-citation network persist and the assumption that one reflects the other is not supported.
  17. Raan, A.F.J. van: Performance-related differences of bibliometric statistical properties of research groups : cumulative advantages and hierarchically layered networks (2006) 0.02 
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    Abstract
    In this article we distinguish between top-performance and lower-performance groups in the analysis of statistical properties of bibliometric characteristics of two large sets of research groups. We find intriguing differences between top-performance and lower-performance groups, and between the two sets of research groups. These latter differences may indicate the influence of research management strategies. We report the following two main observations: First, lower-performance groups have a larger size-dependent cumulative advantage for receiving citations than top-performance groups. Second, regardless of performance, larger groups have fewer not-cited publications. Particularly for the lower-performance groups, the fraction of not-cited publications decreases considerably with size. We introduce a simple model in which processes at the microlevel lead to the observed phenomena at the macrolevel. Next, we fit our findings into the novel concept of hierarchically layered networks. In this concept, which provides the infrastructure for the model, a network of research groups constitutes a layer of one hierarchical step higher than the basic network of publications connected by citations. The cumulative size advantage of citations received by a group resembles preferential attachment in the basic network in which highly connected nodes (publications) increase their connectivity faster than less connected nodes. But in our study it is size that causes an advantage. In general, the larger a group (node in the research group network), the more incoming links this group acquires in a nonlinear, cumulative way. Nevertheless, top-performance groups are about an order of magnitude more efficient in creating linkages (i.e., receiving citations) than lower-performance groups. This implies that together with the size-dependent mechanism, preferential attachment, a quite common characteristic of complex networks, also works. Finally, in the framework of this study on performance-related differences of bibliometric properties of research groups, we also find that top-performance groups are, on average, more successful in the entire range of journal impact.
  18. Lewison, G.: ¬The work of the Bibliometrics Research Group (City University) and associates (2005) 0.02 
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    Date
    20. 1.2007 17:02:22
  19. Li, J.; Willett, P.: ArticleRank : a PageRank-based alternative to numbers of citations for analysing citation networks (2009) 0.02 
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    Abstract
    Purpose - The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations. Design/methodology/approach - ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets - a citation network based on an early paper on webometrics, and a self-citation network based on the 19 most cited papers in the Journal of Documentation - using citation data taken from the Web of Knowledge database. Findings - ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited and ArticleRank is greatest for the most heavily cited articles in a dataset. Originality/value - This is a novel application of the PageRank algorithm.
  20. Vinkler, P.: ¬The institutionalization of scientific information : a scientometric model (ISI-S Model) (2002) 0.02 
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    Abstract
    A scientometric model (ISI-S model) is introduced for describing the institutionalization process of scientific information. The central concept of ISI-S is that the scientific information published may develop with time through permanent evaluation and modification processes toward a cognitive consensus of distinguished authors of the respective scientific field or discipline. ISI-S describes the information and knowledge systems of science as a global network of interdependent information and knowledge clusters that are dynamically changing by their content and size. ISI-S assumes sets of information with short- or long-term impact and information integrated into the basic scientific knowledge or common knowledge. The type of the information sources (e.g., lecture, journal paper, review, monograph, book, textbook, lexicon) and the length of the impact are related to the grade of institutionalization. References are considered as proofs of manifested impact. The relative and absolute development of scientific knowledge seems to be slower than the increase of the number of publications.

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