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Study of Evolving Co-Authorship Network: Identification of Growth Patterns of Collaboration Using SNA Measures

机译:不断发展的共同作者网络研究:使用SNA措施识别协作的增长模式

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In the recent past, macro-level measures of the network have become more popular within complex social networks such as detection of patterns of growth in social network, a community structure or a Heavy-tailed degree distribution. These measures have been reinvented to gain more insight into structural properties due to the fact that many mathematical models do not show these features. This article has studied complex co-authorship network via same classic approach for finding the corresponding properties like authorship patterns, trends of collaboration, the evolution of core component and ranking authors. In this article, we have also analyzed network diameter, clustering coefficient and degree distribution in time span of 50 years and suggested that these measures can be useful indicators for the patterns of connectivity, identifying small world phenomena and identifying the scale-free property of network.
机译:在最近的过去,网络的宏观度量在复杂的社交网络中变得越来越流行,例如检测社交网络的增长模式,社区结构或重尾度分布。由于许多数学模型都没有显示这些特征,因此对这些措施进行了重新发明,以更深入地了解结构特性。本文通过相同的经典方法研究了复杂的共同作者网络,以查找相应的属性,例如作者模式,协作趋势,核心组成部分的演化和排名作者。在本文中,我们还分析了50年时间跨度内的网络直径,聚类系数和度分布,并建议这些措施可作为连通性模式,识别小世界现象和识别网络的无标度特性的有用指标。 。

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