| |  | Shibata, Naoki | Topological analysis of citation networks to discover the future core articles: Research Articles read moreAbstract: In this article, we investigated the factors determining the capability of academic articles to be cited in the future using a topological analysis of citation networks. The basic idea is that articles that will have many citations were in a “similar†position topologically in the past. To validate this hypothesis, we investigated the correlation between future times cited and three measures of centrality: clustering centrality, closeness centrality, and betweenness centrality. We also analyzed the effect of aging as well as of self-correlation of times cited. Case studies were performed in the two following recent representative innovations: Gallium Nitride and Complex Networks. The results suggest that times cited is the main factor in explaining the near future times cited, and betweenness centrality is correlated with the distant future times cited. The effect of topological position on the capability to be cited is influenced by the migrating phenomenon in which the activated center of research shifts from an existing domain to a new emerging domain | 2007 |
| | | Ichise, R. | Research Mining using the Relationships among Authors, Topics and Papers read moreAbstract: As information technology progress, we are able to obtain much information about the advanced research of others. As a result, researchers and research managers need to track the current research trends amid the information flood. In order to support these efforts to gather knowledge of current research, we propose a research trend mining method. The method utilizes an author-topic model for establishing the relationships between authors, topics, and papers by probabilities, and interactively visualizes the relationships using self-organizing maps. We implemented a research area mapping system and validated it with a case study. In addition, we conducted experiments to show the performance of our system. The experimental results indicate that this system can induce the appropriate relationships for finding research trends. | 2007 |
| | | Elmqvist, Niklas | CiteWiz: a tool for the visualization of scientific citation networks read moreAbstract: We present CiteWiz, an extensible framework for visualization of scientific citation networks. The system is based on a taxonomy of citation database usage for researchers, and provides a timeline visualization for overviews and an influence visualization for detailed views. The timeline displays the general chronology and importance of authors and articles in a citation database, whereas the influence visualization is implemented using the Growing Polygons technique, suitably modified to the context of browsing citation data. Using the latter technique, hierarchies of articles with potentially very long citation chains can be graphically represented. The visualization is augmented with mechanisms for parent-child visualization and suitable interaction techniques for interacting with the view hierarchy and the individual articles in the dataset. We also provide an interactive concept map for keywords and co-authorship using a basic force-directed graph layout scheme. A formal user study indicates that CiteWiz is significantly more efficient than traditional database interfaces for high-level analysis tasks relating to influence and overviews, and equally efficient for low-level tasks such as finding a paper and correlating bibliographical data.Information Visualization (2007) 6, 215-232. doi:10.1057/palgrave.ivs.9500156 | 2007 |
| | | Han, H. | Two supervised learning approaches for name disambiguation in author citations read moreAbstract: Due to name abbreviations, identical names, name misspellings, and pseudonyms in publications or bibliographies (citations), an author may have multiple names and multiple authors may share the same name. Such name ambiguity affects the performance of document retrieval, Web search, database integration, and may cause improper attribution to authors. We investigate two supervised learning approaches to disambiguate authors in the citations. One approach uses the naive Bayes probability model, a generative model; the other uses support vector machines (SVMs) [V. Vapnik (1995)] and the vector space representation of citations, a discriminative model. Both approaches utilize three types of citation attributes: coauthor names, the title of the paper, and the title of the journal or proceeding. We illustrate these two approaches on two types of data, one collected from the Web, mainly publication lists from homepages, the other collected from the DBLP citation databases. | 2004 |
| | | Chen, Chaomei | Visualizing a knowledge domain's intellectual structure read moreAbstract: To make knowledge visualizations clear and easy to interpret, we have developed a method that extends and transforms traditional author co-citation analysis by extracting structural patterns from the scientific literature and representing them in a 3D knowledge landscape | 2001 |
| | | Harter, Stephen P. | Semantic relationships between cited and citing articles in library and information science journals read moreAbstract: The act of referencing another author's work in a scholarly or research paper is usually assumed to signal a direct semantic relationship between the citing and cited work. The present article reports a study that examines this assumption directly. The purpose of the research is to investigate the semantic relationship between citing and cited documents for a sample of document pairs in three journals in library and information science: Library Journal, College and Research Libraries, and Journal of the American Society for Information Science. A macroanalysis, based on a comparison of the Library of Congress class numbers assigned citing and cited documents, and a microanalysis, based on a comparison of descriptors assigned citing and cited documents by three indexing and abstracting journals, ERIC, LISA, and Library Literature, were conducted. Both analyses suggest that the subject similarity among pairs of cited and citing documents is typically very small, supporting a subjective, psychological view of relevance and a trial-and-error, heuristic understanding of the information search and research processes. The results of the study have implications for collection development, for an understanding of psychological relevance, and for the results of doing information retrieval using cited references. Several intriguing methodological questions are raised for future research, including the role of indexing depth, specificity, and quality on the measurement of document similarity | 1993 |
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