This paper studies the structure of collaboration in the Journal of Finance for the period 1980-2009 using publication data from the Social Sciences Citation Index (SSCI). There are 3,840 publications within this period, out of which 58% are collaborations. These collaborations form 405 components, with the giant component capturing approximately 54% of total coauthors (it is estimated that the upper limit of distinct JF coauthors is 2,536, obtained from the total number of distinct author keywords found within the study period). In comparison, the second largest component has only 13 members. The giant component has mean degree 3 and average distance 8.2. It exhibits power-law scaling with exponent a = 3.5 for vertices with degree C5. Based on the giant component, the degree, closeness and betweenness centralization score, as well as the hubs/authorities score is determined. The findings indicate that the most important vertex on the giant component coincides with Sheridan Titman based on his top ten ranking on all four scores.
This paper presents a comparative impact analysis on collaborative research in Malaysia. All analyses were conducted using ISI-indexed journal articles published in the 10-year period spanning the years 2000-2009. The publication growth and distribution of domestic versus international Malaysian-addressed collaborative articles was examined. Then, a three-pronged approach was used to compare the research performance between international and domestic research for the top ten high-productivity subject categories. Firstly, the potentiality of collaborative research impact is determined using the MannWhitney-Wilcoxon and Bootstrap Kolmogorov-Smirnov tests. Then, the Hirsch and Egghe indices were computed for each subject category to estimate the distance needed to bridge the gap between international and domestic research. Lastly, the composition of researchers was measured using the internationality index. We discuss how the findings of our methodology help advise collaborative research strategies that will contribute to better research performance in the leading scientific categories.
This paper undertakes a social network analysis of two science fiction television series, Stargate and Star Trek. Television series convey stories in the form of character interaction, which can be represented as “character networks”. We connect each pair of characters that exchanged spoken dialogue in any given scene demarcated in the television series transcripts. These networks are then used to characterize the overall structure and topology of each series. We find that the character networks of both series have similar structure and topology to that found in previous work on mythological and fictional networks. The character networks exhibit the small-world effects but found no significant support for power-law. Since the progression of an episode depends to a large extent on the interaction between each of its characters, the underlying network structure tells us something about the complexity of that episode’s storyline. We assessed the complexity using techniques from spectral graph theory. We found that the episode networks are structured either as (1) closed networks, (2) those containing bottlenecks that connect otherwise disconnected clusters or (3) a mixture of both.
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