Using Machine Learning to Predict the Evolution of Physics Research

TL;DR

This paper develops a machine learning approach to predict the evolution of physics research topics using bibliometric data, enabling a quantitative understanding of scientific progress and resource allocation.

Contribution

It introduces a novel machine learning framework to forecast the future dynamics of research topics based on bibliometric network features.

Findings

Degree, closeness, and betweenness are key predictive features.

Betweenness increases for merging events and decreases for splitting events.

The method enables predicting whether research topics will continue, merge, split, or dissolve.

Abstract

The advancement of science as outlined by Popper and Kuhn is largely qualitative, but with bibliometric data it is possible and desirable to develop a quantitative picture of scientific progress. Furthermore it is also important to allocate finite resources to research topics that have growth potential, to accelerate the process from scientific breakthroughs to technological innovations. In this paper, we address this problem of quantitative knowledge evolution by analysing the APS publication data set from 1981 to 2010. We build the bibliographic coupling and co-citation networks, use the Louvain method to detect topical clusters (TCs) in each year, measure the similarity of TCs in consecutive years, and visualize the results as alluvial diagrams. Having the predictive features describing a given TC and its known evolution in the next year, we can train a machine learning model to predict future changes of TCs, i.e., their continuing, dissolving, merging and splitting. We found the number of papers from certain journals, the degree, closeness, and betweenness to be the most predictive features. Additionally, betweenness increases significantly for merging events, and decreases significantly for splitting events. Our results represent a first step from a descriptive understanding of the Science of Science (SciSci), towards one that is ultimately prescriptive.