We present a method to analyse the scientific contributions between research groups. Given multiple research groups, we construct their journal proceeding graphs and then compute the similarity gap between them using network analysis. This analysis can be used for measuring similarity gap of the topics qualities between research groups' scientific contributions. We demonstrate the practicality of our method by comparing the scientific contributions by Korean researchers with those by the global researchers for information security in 2006 - 2008. The empirical analysis shows that the current security research in South Korea has been isolated from the global research trend.
In this communication we perform an analysis of European science, investigating the way countries are joined in clusters according to their similarity. An extremely clear pattern arises, suggesting that geographical and cultural factors strongly influence the scientific fabric of these countries. Although it is seen that one of the major factors behind Science in Europe is, apparently, geographical proximity, bilateral cooperation between countries cannot fully account for the respective similarity. Long-term policies, planning and investment are also visible in the results. This paper examines a number of the criticisms that citation analysis has been subjected to over the years. It is argued that many of these criticisms have been based on only limited examinations of data in particular contexts and it remains unclear how broadly applicable these problems are to research conducted at different levels of analysis, in specific field, and among various national data sets. Relevant evidence is provided from analysis of Australian and international data. Citation analysis is likely to be most reliable when data is aggregated and at the highly-cited end of the distribution. It is possible to make valid inferences about individual cases, although considerable caution should be used. Bibliometric measures should be viewed as a useful supplement to other research evaluation measures rather than as a replacement. Reassessing the assessment criteria and techniques traditionally used in evaluating computer science research effectiveness.  Research evaluation is based on a representation of the research. Improving the quality of the representations cannot prevent the indicators from being provided with meaning by a receiving discourse different from the research system(s) under study. Since policy decisions affect the systems under study, this configuration generates a tension that has been driving the further development of science indicators since World War II. The article discusses historically the emergence of science indicators and some of the methodological problems involved. More recent developments have been induced by the emergence of the European Union as a supra-national level of policy coordination and by the Internet as a global medium of communication. As science, technology, and innovation policies develop increasingly at various levels and with different objectives, the evaluative discourses can be expected to differentiate with reference to the discourses in which they are enrolled.