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

Abstract

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.