Hierarchical networks of scientific journals

TL;DR

This paper constructs hierarchical networks of scientific journals based on citation data, revealing complex influence relations and differences between two hierarchy extraction methods, thus providing a detailed view of journal interrelations.

Contribution

It introduces a novel approach to mapping journal influence hierarchies using citation-based metrics and compares two different hierarchy extraction methods.

Findings

Hierarchical networks reveal non-trivial relations among journals.

Two hierarchy extraction methods produce similar but distinct structures.

Networks show the influence and information spread among journals.

Abstract

Scientific journals are the repositories of the gradually accumulating knowledge of mankind about the world surrounding us. Just as our knowledge is organised into classes ranging from major disciplines, subjects and fields to increasingly specific topics, journals can also be categorised into groups using various metrics. In addition to the set of topics characteristic for a journal, they can also be ranked regarding their relevance from the point of overall influence. One widespread measure is impact factor, but in the present paper we intend to reconstruct a much more detailed description by studying the hierarchical relations between the journals based on citation data. We use a measure related to the notion of m-reaching centrality and find a network which shows the level of influence of a journal from the point of the direction and efficiency with which information spreads through the network. We can also obtain an alternative network using a suitably modified nested hierarchy extraction method applied to the same data. The results are weakly methodology-dependent and reveal non-trivial relations among journals. The two alternative hierarchies show large similarity with some striking differences, providing together a complex picture of the intricate relations between scientific journals.