Citation likelihood analysis of the interbank financial networks literature: A machine learning and bibliometric approach

Abstract

The interbank financial networks literature has been gaining ground since the 2007–2008 global financial crisis. This paper contributes to the literature of interbank financial networks by summarizing its trends and patterns of published scientific papers using a bibliometric complex network approach. We also provide a citation likelihood analysis of papers in this literature using predictive machine learning algorithms. Even after a decade from the global financial crisis, we find that the literature has been growing significantly in recent years and as an interdisciplinary area. We find that single-authored and the keyword “liquidity” strongly predict more citations for papers in this literature. Our analysis has practical implications for practitioners and academic staff as it provides guidelines for the hot topics most valued by the community researching interbank financial networks. Moreover, we identify the most preeminent papers, authors, and journal outlets in this literature over time.