Time dependent analysis and prediction of financial networks using supervised latent feature relational models

Abstract

In recent years, many researchers have taken keen interest in analyzing various kinds of relational data, such as social networks and financial networks. These data can be expressed as a graph or network where each vertex or node is an entity and each edge or link is a relation between a pair of entities. Moreover, each link is often associated with continuous and/or discrete relational attributes, such as in financial networks, the interest rate for a transaction and whether the transaction is international or intranational. In this paper we focus on max-margin latent feature relational models (called Med-LFRM) that are based on Indian buffet process (IBP) and maximum entropy discrimination (MED). For the estimation of model parameters, the Bayesian estimation is deemed equivalent to minimizing an objective function, which involves misclassification errors. We focus on link prediction problem for the networks with continuous and discrete relational attributes. We also focused on the time dependent analysis for the networks, and therefore, we estimated the model parameters considering the observations in the previous time interval. We demonstrate, through experiments with inter-bank financial networks, the effectiveness of the above model in terms of the link prediction performance.