Clusters and Maps of Science Journals Based on Bi-connected Graphs in the Journal Citation Reports

TL;DR

This paper introduces a graph-analytical method using bi-connected components to classify and visualize science journals based on citation data, revealing their structural relationships and overlaps.

Contribution

It applies bi-connected graph analysis to journal citation networks, providing a novel classification and visualization approach for scientific journals.

Findings

Clusters vary in size and internal density.

Articulation points indicate overlaps but not a central 'general science' cluster.

The method enables detailed journal classification and mapping.

Abstract

The aggregated journal-journal citation matrix derived from the Journal Citation Reports 2001 can be decomposed into a unique subject classification by using the graph-analytical algorithm of bi-connected components. This technique was recently incorporated in software tools for social network analysis. The matrix can be assessed in terms of its decomposability using articulation points which indicate overlap between the components. The articulation points of this set did not exhibit a next-order network of 'general science' journals. However, the clusters differ in size and in terms of the internal density of their relations. A full classification of the journals is provided in an Appendix. The clusters can also be extracted and mapped for the visualization.