  In this paper we present a novel algorithm to study the evolution of credit
risk across complex multilayer networks. Pagerank-like algorithms allow for the
propagation of an influence variable across single networks, and allow
quantifying the risk single entities (nodes) are subject to given the
connection they have to other nodes in the network. Multilayer networks, on the
other hand, are networks where subset of nodes can be associated to a unique
set (layer), and where edges connect elements either intra or inter networks.
Our personalized PageRank algorithm for multilayer networks allows for
quantifying how credit risk evolves across time and propagates through these
networks. By using bipartite networks in each layer, we can quantify the risk
of various components, not only the loans. We test our method in an
agricultural lending dataset, and our results show how default risk is a
challenging phenomenon that propagates and evolves through the network across
time.
