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.
An alternative approach is to analyse researchers' social networks such as co-citation networks @cite_2 @cite_24 @cite_27 @cite_5 and co-authorship networks @cite_28 @cite_22 @cite_39 @cite_33 . Citation networks can be also used to evaluate the importance of journals proceedings by computing centrality values of the nodes in a citation graph. Co-authorship networks are an important class of social networks and have been used extensively. Many co-authorship networks have been studied to investigate the patterns, motivation, and the structure of scientific collaboration @cite_18 @cite_34 @cite_32 @cite_20 @cite_12 @cite_7 @cite_10 @cite_3 . Morris @cite_31 proposed a model to monitor the birth and development of a scientific speciality with a collection of journal papers. Lee @cite_35 practically analysed the research trends in the information security field using co-word analysis''. Our work is to extend these to measure the similarity gap between research groups by comparing their publication outputs.
@cite_35 @cite_18 @cite_22 @cite_33 @cite_7 @cite_28 @cite_32 @cite_3 @cite_39 @cite_24 @cite_27 @cite_2 @cite_5 @cite_31 @cite_34 @cite_10 @cite_12 @cite_20
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In the highly competitive world, there has been a concomitant increase in the need for the research and planning methodology, which can perform an advanced assessment of technological opportunities and an early perception of threats and possibilities of the emerging technology according to the nation’s economic and social status. Abstract Patrick Ion (Mathematical Reviews) and Jerry Grossman (Oakland University) maintain a collection of data on Paul Erdos, his co-authors and their co-authors. These data can be represented by a graph, also called the Erdos collaboration graph. In this paper, some techniques for analysis of large networks (different approaches to identify ‘interesting’ individuals and groups, analysis of internal structure of the main core using pre-specified blockmodeling and hierarchical clustering) and visualizations of their parts, are presented on the case of Erdos collaboration graph, using the program Pajek . In this paper, we describe the evolution and impact of computer-supported cooperative work (CSCW) research through social network analysis of coauthorship data. A network of authors as nodes and shared papers as links is used to compare patterns of growth and collaboration in CSCW with other domains, such as high-energy physics and computer science. Further, the coauthorship network data are used to depict dynamic changes in the structure of CSCW collaborations over time. Examination of these changes shows high volatility in the composition of the CSCW research community over decade-long time spans. These data are augmented by a brief citation analysis of recent CSCW conferences. We discuss the implications of the CSCW findings in terms of the influence of CSCW research on the larger field of HCI research as well as the general utility of social network analysis for understanding patterns of collaboration. This paper presents the analysis and modelling of two data sets associated with the literature of hypertext as represented by the ACM Hypertext conference series. This work explores new ways of organising and accessing the vast amount of interrelated information. The first data set, including all the full papers published in this series (1987 1998), is structured and visualised as a semantic space. This semantic space provides an access point for each paper in this collection. The second data set, containing author co-citation counts based on nine conferences in the series (1989 1998), is analysed and mapped in its entirety and in three evenly distributed sub-periods. Specialties  major research fronts in the field of hypertext  are identified based on the results of a factor analysis and corresponding author co-citation maps. The names of authors in these maps are linked to the bibliographical and citation summaries of these authors on the WWW. A model is presented of the manifestation of the birth and development of a scientific specialty in a collection of journal papers. The proposed model, Cumulative Advantage by Paper with Exemplars (CAPE) is an adaptation of Price's cumulative advantage model (D. Price, [1976]. Two modifications are made: (a) references are cited in groups by paper, and (b) the model accounts for the generation of highly cited exemplar references immediately after the birth of the specialty. This simple growth process mimics many characteristic features of real collections of papers, including the structure of the paper-to-reference matrix, the reference-per-paper distribution, the paper-per-reference distribution, the bibliographic coupling distribution, the cocitation distribution, the bibliographic coupling clustering coefficient distribution, and the temporal distribution of exemplar references. The model yields a great deal of insight into the process that produces the connectedness and clustering of a collection of articles and references. Two examples are presented and successfully modeled: a collection of 131 articles on MEMS RF (microelectromechnical systems radio frequency) switches, and a collection of 901 articles on the subject of complex networks. © 2005 Wiley Periodicals, Inc.