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.

自2007-2008年全球金融危机以来，银行间金融网络文献一直在发展。本文通过使用文献计量复杂网络方法总结了银行间金融网络的趋势和已发表的科学论文的模式，为文献研究做出了贡献。我们还使用预测性机器学习算法对本文中的论文进行了引用可能性分析。即使在经历了全球金融危机的十年之后，我们也发现近年来作为跨学科领域的文献已经显着增长。我们发现，单著者和关键字“流动性”强烈预测了该文献中论文的更多引用。我们的分析对从业人员和学术人员具有实际意义，因为它为社区研究银行间金融网络最重视的热点话题提供了指导。此外，随着时间的推移，我们确定了该文献中最杰出的论文，作者和期刊来源。