Accidentality in journal citation patterns

We study an agent-based model for generating citation distributions in complex networks of sci-entific papers, where a fraction of citations is allotted according to the preferential attachment rule (rich get richer) and the remainder is allocated accidentally (purely at random, uniformly). Previously, we derived and analysed such a process in the context of describing individual au-thors, but now we apply it to scientific journals in computer and information sciences. Based on the large DBLP dataset as well as the CORE (Computing Research and Education Association of Australasia) journal ranking, we find that the impact of journals is correlated with the degree of accidentality of their citation distribution. Citations to impactful journals tend to be more pref-erential, while citations to lower-ranked journals are distributed in a more accidental manner. Further, applied fields of research such as artificial intelligence seem to be driven by a stronger preferential component - and hence have a higher degree of inequality - than the more theoretical ones, e.g., mathematics and computation theory.