A unified approach to mapping and clustering of bibliometric networks

TL;DR

This paper introduces a unified method for simultaneously mapping and clustering bibliometric networks, based on a common underlying principle, demonstrated on citation data in information science.

Contribution

It presents a novel unified framework that derives both mapping and clustering techniques from the same principle, improving consistency in bibliometric analysis.

Findings

Unified approach effectively combines mapping and clustering.

Application to citation data reveals meaningful structures.

Method enhances interpretability of bibliometric networks.

Abstract

In the analysis of bibliometric networks, researchers often use mapping and clustering techniques in a combined fashion. Typically, however, mapping and clustering techniques that are used together rely on very different ideas and assumptions. We propose a unified approach to mapping and clustering of bibliometric networks. We show that the VOS mapping technique and a weighted and parameterized variant of modularity-based clustering can both be derived from the same underlying principle. We illustrate our proposed approach by producing a combined mapping and clustering of the most frequently cited publications that appeared in the field of information science in the period 1999-2008.