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An Evolving Hypernetwork Model to Quantify Progress Potential of Emerging Research Topic

机译:一种不断发展的超网络模型,以量化新兴研究主题的进展潜力

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There is considerable and growing interest in the emergence of research topics. However, current methods to detect the emergence are still problematic mainly due to information loss and aging effect. In this study, we show three intrinsic mechanisms including preferential attachment, exponentially growth and heterogeneous fitness values that decay with time. Depending on the input assumptions, all topics tend to follow a universal temporal pattern according to our model which results in strongly sufficiency to quantify progress potential.
机译:对研究主题的出现有相当大的兴趣。然而,检测出现的当前方法仍然是由于信息损失和老化效应的问题。在这项研究中,我们展示了三种内在机制,包括衰减时间的优先附着,指数增长和异质性能值。根据输入假设,所有主题往往根据我们的模型遵循通用的时间模式,这导致量化进展潜力的强烈充足。

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