Tracing the evolution of physics with a keyword co-occurrence network

TL;DR

This paper presents a method to analyze the evolution of physics sub-fields by constructing and examining keyword co-occurrence networks, revealing community structures and their temporal changes.

Contribution

It introduces a novel approach to build and analyze keyword networks from limited initial data, effectively identifying sub-fields and their evolution over time.

Findings

Successfully identified scientific sub-fields through community detection.

Tracked the temporal evolution of research communities.

Constructed relevant keyword networks from small initial sets.

Abstract

Describing the evolution of science is a salient work not only for revealing the scientific trend but also for establishing a scientific classification system. In this paper, we investigate the evolution of science by observing the structure and change of keyword co-occurrence networks. Starting from seven target physics fields and their initial keywords selected by experts from the Korean Physical Society, we generate keyword co-occurrence networks better to capture topological structure with our proposed approach. In this way, we can construct a more relevant and abundant keyword network from a small set of initial keywords. With these networks, we successfully identify the scientific sub-field by detecting communities and extracting core keywords of each community. Furthermore, we trace the temporal evolution of sub-fields with the time-snapshot keyword network, the resultant temporal change of the community membership explains the evolution of the research field well. Our approach for tracing the evolution of the research field with a keyword co-occurrence network can shed light on identifying and assessing the evolution of science.