Latent Transaction Prediction Using Graph Embedding

Abstract

Analysis of transaction data between corporations has strong future growth potential in finance since financial institutions have the large amount of the data. However, since it is difficult for each institution to get such data except the main customers' one, the data is partial and the application would be also limited. We set a similar environment artificially from the only observed data by removing some observed transaction links, and evaluate if we could predict the removed links. We show that graph embedding could lead to a solution of this problem.