Prediction Methods and Applications in the Science of Science: A Survey

TL;DR

This survey reviews data-driven prediction methods in science of science, focusing on citation and scholar impact prediction, highlighting recent advances, challenges, and future research directions.

Contribution

It provides a comprehensive overview of prediction techniques and applications in science of science, emphasizing recent developments and open challenges.

Findings

Citation prediction methods improve impact forecasting.

Scholar impact prediction enhances academic evaluation.

Collaboration network analysis promotes scholarly cooperation.

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.