Abstract:
We identify two orthogonal journal performance indicators from the points of view of size-dependence and principal component analysis using graph-theoretic constructs from social network analysis. One, the power-weakness ratio is a size-independent recursive proxy for the quality of the journal's performance in the network. The second, the number of references (out-links) that the journal makes to all journals in the network is the size-dependent proxy for the size of the journal (a quantity metric). In an input-output sense, the number of references becomes the measure of the input and the number of citations received by the journal from all journals in the network becomes the size-dependent measure of the output. The power- weakness ratio of citations to references before recursive iteration becomes the non-network measure of popularity and the power-weakness ratio of weighted citations and weighted references after recursive iteration becomes the network measure of prestige of the journal. It is also possible to propose first-order and second-order measures of influence which are products of the quality and quantity parameter space. We also show that the influence weight that emerges from a Pinski-Narin or Google PageRank formulation is a size-dependent measure of prestige that is orthogonal to the power-weakness ratio. We illustrate the concepts using two simple artificial two- and threejournal networks and a real-life example of a subgraph of 10 well-known statistical journals with network data collected from the Web of Science.
