Prediction methods and applications in the science of science: A survey

Abstract

Science of science has become a popular topic that attracts great attentions from the research community. The development of data analytics technologies and the readily available scholarly data enable the exploration of data-driven prediction, which plays a pivotal role in finding the trend of scientific impact. In this paper, we analyse methods and applications in data-driven prediction in the science of science, and discuss their significance. First, we introduce the background and review the current state of the science of science. Second, we review data-driven prediction based on paper citation count, and investigate research issues in this area. Then, we discuss methods to predict scholar impact, and we analyse different approaches to promote the scholarly collaboration in the collaboration network. This paper also discusses open issues and existing challenges, and suggests potential research directions.

Visualization of Research Trending Topic Prediction: Intelligent Method for Data Analysis

“…In their survey [5], Houa and others (2019) analyzed methods and applications in data-driven prediction in the science of science and discussed their significance. They summarized the research issues from three perspectives: the papers' impact prediction, scholar impact prediction, and author collaboration prediction.…”

Data-Driven Computational Social Science: A Survey

“…The social proximity between individuals is driven not only by homophilous relations, but also by their interactions. Friendship prediction is strongly related to the interactions between users across the various forms of communication [60]. There is a higher possibility of becoming friends and intimate partners with greater the frequency of interactions.…”

How can citation impact in bibliometrics be normalized? A new approach combining citing-side normalization and citation percentiles

“…A possible solution to these problems might be to avoid the use of field categorization schemes (Bornmann, Marx, et al, 2013), clustering (Waltman & van Eck, 2012), and similarity approaches (Colliander & Ahlgren, 2019), and for each focal paper (that is assessed) to manually search some papers for comparison that are thematically similar (Kostoff, 2002;Waltman, 2016). This solution corresponds to the judgement by Hou, Pan, et al (2019) that field normalization cannot be solved by statistical techniques. The manual collection of papers for the comparison with a focal paper might be possible in the evaluation of small sets of papers; however, it is not practicable for large sets (e.g., all papers published by a university over several years).…”