论文标题
使用个性化Pagerank算法的多层网络的信用风险演变
Evolution of Credit Risk Using a Personalized Pagerank Algorithm for Multilayer Networks
论文作者
论文摘要
在本文中，我们提出了一种新型算法，以研究复杂多层网络中信用风险的演变。 Pagerank样算法允许在单个网络之间传播影响变量，并且允许量化风险单实体（节点）的范围可能给定其与网络中其他节点所具有的连接。另一方面，多层网络是网络，可以将节点子集与唯一集合（层）相关联，并且边缘连接元素Intra或Inter网络。我们用于多层网络的个性化Pagerank算法允许量化信用风险在时间上演变并通过这些网络传播。通过在每一层中使用双方网络，我们可以量化各种组件的风险，而不仅仅是贷款。我们在农业贷款数据集中测试了我们的方法，我们的结果表明，默认风险是一种充满挑战的现象，可以随着时间的推移通过网络传播和发展。
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.
