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Evolution of scientific collaboration networks

机译:科学合作网络的演变

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We study the structure and evolution of scientific collaboration network by using collaboration network constructed from DBLP Computer Science Bibliographic database [1], from year 1936 to 2013 using social network analysis techniques. We have found many interesting features such as collaboration between scientists is increasing with time and few numbers of scholars publish a large number of papers while most of the authors publish a small number of papers, which is consistent with Lotka's law on frequency of publications [2]. The degrees of the vertices in the collaboration graph follow a “Power law” pattern i.e., the number of vertices of degree x is proportional to a negative power of x. The clustering coefficient of collaboration graph comes out to be very high which means that there are more chances for two authors to co-author a paper if they have a common collaborator. We also found that the collaboration graph follows various real graph properties like WPL (Weight power law), DPL (Densification power law) etc. We try to apply the Lorenz curve and Gini coefficient on the collaboration graph to study the variation in concentration of collaboration between researchers with time.
机译:我们使用社会网络分析技术,使用从1936年至2013年间由DBLP计算机科学书目数据库[1]构建的协作网络,研究科学协作网络的结构和演化。我们发现许多有趣的功能,例如科学家之间的合作随着时间的推移而增加,很少有学者发表大量论文,而大多数作者发表的论文数量很少,这与洛特卡关于出版频率的定律是一致的[2 ]。协作图中顶点的度数遵循“幂定律”模式,即,度数x的顶点数与x的负度数成正比。协作图的聚类系数非常高,这意味着如果两位作者有共同的协作者,则有更多机会共同撰写论文。我们还发现协作图遵循各种真实的图属性,例如WPL(权重定律),DPL(密度幂定律)等。我们尝试在协作图上应用Lorenz曲线和Gini系数来研究协作集中度的变化与时间之间的研究人员之间。

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