Network Embedding using Variational Autoencoder and its Application to Financial Networks

Abstract

In this paper, we present low-dimensional embedding methods for interbank transaction networks. To address one important problem: how to obtain latent representations that well capture the structual properties of a given directed network, we propose a new network embedding model, Co-Variational Autoencoder (Co-VAE). Co-VAE simultaneously learns network embedding focusing on the links going into each node and that focusing on the links coming out of each node, attempting to reproduce the original adjacency matrix. Thereby, we can learn the Co-VAE network embedding model, simultaneously capturing both the latent representations of lender patterns and those borrower patterns. Using both latent representations, we can predict interest rates of interbank transactions.