A review of scientific impact prediction: tasks, features and methods

With the rapid evolution of scientific research, there are a huge volume of papers published every year and the number of scholars is also growing fast. How to effectively predict the scientific impact has become an important research problem, attracting the attention of researchers in various fields, and it is of great significance in improving research efficiency and assisting in decision-making and scientific evaluation. In this paper, we propose a new framework to perform a systematical survey of scientific impact prediction research. Specifically, we take the four common academic entities into account: papers, scholars, venues and institutions. We reviewed all the prediction tasks reported in the literature in detail; the input features are divided into six groups: paper-related, author-related, venue-related, institution-related, network-related and altmetrics-related. Moreover, we classify the forecasting methods into mathematical statistics-based, traditional machine learning-based, deep learning-based and graph-based, and subdivide each category according to the characteristics. Finally, we discuss open issues and existing challenges, and provide potential research directions.

Automated summary

In this paper, we propose a new framework for systematically surveying scientific impact prediction research. We consider the four common academic entities: papers, scholars, venues, and institutions. We review all reported prediction tasks and categorize input features into six groups. Furthermore, we classify forecasting methods into different categories and subdivisions based on their characteristics. Finally, we discuss open issues, existing challenges, and potential research directions.