Massive-Scale Graph Mining Technique for Entrepreneurial Debt Analysis

Investigating credit risk in contemporary entrepreneurial debt analysis is the key for maintaining financial stability. Entrepreneurial debt management, facilitated by innovative financing models, is significant in fostering economic as well as social development. However, due to certain imperfections in these financial systems, this model is exposed to considerable risks. To precisely predict the risk level of each entrepreneurial debt, this paper presents a novel methodology combining a massive-scale affinity graph construction and efficient subgraph mining for categorizing each entrepreneurial debt into one of the multiple debt-level subgraphs. This approach marks a substantial contribution to the advancement and generalization of entrepreneurial debt management systems. For extensively testifying our approach, we collected a huge data set containing entrepreneurial debt information from nearly 1.41 million company with different sizes throughout the world. Our experimental results showed that our designed method achieves an impressive 97.93% accuracy in predict different risk levels in entrepreneurial debt management, highlighting its efficacy and potential for broad implementation.