Bibliometric impact measures leveraging topic analysis
Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries
Using field cocitation analysis to assess reciprocal and shared impact of LIS-MIS fields
Journal of the American Society for Information Science and Technology
Toward a consensus map of science
Journal of the American Society for Information Science and Technology
Comparative study on methods of detecting research fronts using different types of citation
Journal of the American Society for Information Science and Technology
Showing the essential science structure of a scientific domain and its evolution
Information Visualization
Measuring CMOT's intellectual structure and its development
Computational & Mathematical Organization Theory
Computational historiography: Data mining in a century of classics journals
Journal on Computing and Cultural Heritage (JOCCH)
A new methodology for constructing a publication-level classification system of science
Journal of the American Society for Information Science and Technology
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Measuring the relatedness between bibliometric units (journals, documents, authors, or words) is a central task in bibliometric analysis. Relatedness measures are used for many different tasks, among them the generating of maps, or visual pictures, showing the relationship between all items from these data. Despite the importance of these tasks, there has been little written on how to quantitatively evaluate the accuracy of relatedness measures or the resulting maps. The authors propose a new framework for assessing the performance of relatedness measures and visualization algorithms that contains four factors: accuracy, coverage, scalability, and robustness. This method was applied to 10 measures of journal–journal relatedness to determine the best measure. The 10 relatedness measures were then used as inputs to a visualization algorithm to create an additional 10 measures of journal–journal relatedness based on the distances between pairs of journals in two-dimensional space. This second step determines robustness (i.e., which measure remains best after dimension reduction). Results show that, for low coverage (under 50%), the Pearson correlation is the most accurate raw relatedness measure. However, the best overall measure, both at high coverage, and after dimension reduction, is the cosine index or a modified cosine index. Results also showed that the visualization algorithm increased local accuracy for most measures. Possible reasons for this counterintuitive finding are discussed. © 2006 Wiley Periodicals, Inc.