A reference-based Hirschian similarity measure for journals

Abstract

Hirsch's concept of h-index was used to define a similarity measure for journals. The h-similarity is easy to calculate from the publicly available data of the Journal Citation Reports, and allows for plausible interpretation. On the basis of h-similarity, a relative eminence indicator of journals was determined: the ratio of the JCR impact factor to the weighted average of that of similar journals. This standardization allows journals from disciplines with lower average citation level (mathematics, engineering, etc.) to get into the top lists.

An Empirical Test of the Inter-Relationships between Various Bibliometric Creative Scholarship Indicators

“…Disparity for different cited journals and first authors was based on Schubert's [57] Hirschian similarity measure and Jaccard similarity based on an author coupling network matrix, as implemented in the bibliometrix R package [49], respectively. The Hirschian similarity between journals requires downloading of the Citing Journal Data from Journal Citation Reports (JCR; https://jcr.clarivate.com), which include a list of journals cited by a given journal.…”

“…The Hirschian similarity between journals requires downloading of the Citing Journal Data from Journal Citation Reports (JCR; https://jcr.clarivate.com), which include a list of journals cited by a given journal. The h-cores from these lists of any two pair of journals are then used to derive a Jaccard-type similarity measure between journals [57]. First, the creativity dataset was scored.…”

h-Degree as a basic measure in weighted networks

“…In 2009, Schubert, Korn and Telcs ) introduced the lobby index as a centrality parameter for nodes and the h-index of a network as an indicator for complete networks (Schubert & Soos, 2010). The lobby index of node n is defined as the largest integer k such that n has at least k neighbors with a degree, d(n), of at least k. The h-index of a network is defined as, "A network's h-index is h if not more than h of its nodes have a degree not less than h" (Schubert, 2010). These indicators can be further developed leading to enriched network analysis methods in numerous applications (Rousseau & Ye, 2011).…”

Mapping of science journals based on h-similarity

“…Recently (Schubert 2010), a similarity measure of journals has been introduced on the basis of the h-index concept of Hirsch (2005) applied to the co-reference strength of journal pairs. In this paper an attempt is made to use this h-similarity measure for clustering science journals, and thereby to gain a structural map of science fields.…”

“…Schubert In (Schubert 2010), the h-similarity measure was found to be useful in defining comparison standards for journal citation rates (such as impact factors), and the possibility of using it as a basis for thematic clustering of journals has been raised.…”