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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测量识别合作的共同性能模式

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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,网络的宏观级别措施在复杂的社交网络中变得更加流行,例如检测社交网络的增长模式,社区结构或重尾度分布。由于许多数学模型不显示这些功能,这些措施已经重新发明,以获得更高的结构性属性。本文通过相同的经典方法研究了复杂的共同作者网络,用于查找作者模式等相应属性,协作趋势,核心组件和排名作者的演变。在本文中,我们还分析了网络直径,聚类系数和程度分布在50年的时间范围内,并建议这些措施可以是连接性模式的有用指标,识别小世界现象并识别网络的无规模性财产。

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