Role-Aware Backbone Extraction and Visualization of Financial Transaction Networks via Asymmetric Non-negative Matrix Factorization

Abstract

This paper proposes a novel backbone extraction framework tailored for financial transaction networks (FTNs), which are inherently directed, weighted, and often dense. Traditional backbone extraction methods typically assume undirected or symmetric structures and struggle to capture the role-specific, directional nature of financial data. To address this issue, we introduce an Asymmetric Non-negative Matrix Factorization (Asymmetric NMF) technique that decomposes FTNs into low-rank representations, preserving directional features while simplifying network complexity. This method effectively isolates the most significant inter-firm financial relationships and identifies influential firms from both buyer and seller perspectives. The model is validated using real-world industrial financial transaction data, demonstrating its superiority over existing backbone extraction methods in interpretability and structural fidelity.