A Novel Similarity Score for Link Prediction Approach Using Financial Transaction Networks and Firms’ Attribute

Abstract

Financial transaction networks represent inter-firm relationships, where firms and transactions act as nodes and edges, respectively. Link prediction in these networks aims to identify potential future or missing transactions or links, providing valuable insights for decision-making and market analysis. While several link prediction studies exist for general networks, limited research has specifically addressed the unique characteristics of financial transaction networks. Existing studies often overlook important features such as the direction of transactions between firms, the hierarchical nature of transaction networks, and the significance of node attributes, thereby hindering accurate link prediction. In this study, we propose a novel similarity score, the “Attribute-Transaction Similarity (ATS) Score,” for link prediction in financial transaction networks. The ATS Score integrates both transaction network topology and firm attributes, such as the Standard Industrial Classification (SIC) codes, to predict unobserved links between firms. Our method not only forecasts future transactions but also preserves the hierarchical structure of transaction networks. By leveraging both network topology and firm attribute frequencies, our method results in more accurate and reliable predictions. Experimental evaluations on real-world financial transaction network datasets demonstrate that the ATS Score-based link prediction method outperforms existing similarity-based link prediction techniques, achieving superior results in terms of the area under the receiver operating characteristic curve (AUC). This highlights the effectiveness of the ATS Score in capturing the intricate relationships and dynamics of financial transaction networks.