Empirical Network Graph Analysis of Blockchain Ledger Ecosystems

• Applicant: Professor Dr. Oliver Hinz
• Subject Area: Operations Management and Computer Science for Business Administration ; Data Management, Data-Intensive Systems, Computer Science Methods in Business Informatics
• Term: since 2026
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 566259616

Blockchain ledger ecosystems present unique empirical challenges and opportunities in economic research. While traditional financial and social networks often suffer from limited data availability, blockchain networks generate vast amounts of publicly accessible transaction data. These networks capture complex economic relationships with unprecedented detail and transparency. However, current research approaches have not fully leveraged this potential, as traditional econometric methods often reduce rich network data to simplified metrics, overlooking complex structural and temporal patterns with dynamic relationships. The challenges are multifaceted: blockchain transactions form multi-layered networks where users interact through various protocols; these networks evolve rapidly with temporal dynamics crucial for understanding market formation and protocol adoption; the scale of blockchain data presents significant computational challenges; and the pseudonymous nature of transactions creates unique challenges in linking network patterns to economic outcomes. This research proposal translates these challenges into two interconnected objectives: 1) establishing a comprehensive graph-based methodology for blockchain economic research and 2) demonstrating its practical application through three empirical investigations. The proposal addresses these objectives by four working packages (WP) in the course of 36 months. The first working package (WP1) develops a Python-based analytical toolkit for blockchain network dynamics, incorporating temporal network analysis and causal inference techniques. The following WP apply these methods in empirical settings. WP2 examines reputation systems in decentralized lending, focusing on predictive models for loan defaults and improvements in collateral requirements to enhance capital efficiency. WP3 explores decentralized social networks, analysing the transformation of social capital into economic value through network position-token value relationships. WP4 investigates marketing effectiveness, evaluating how network structures influence user engagement patterns and the impact of token-based marketing strategies on protocol adoption. The expected outcomes encompass both theoretical advances in methodological tools and empirical insights while providing highly practical applications for improving capital efficiency in decentralized lending markets, enhancing social capital valuation in decentralized networks, and developing effective blockchain-based marketing strategies. The Taiwan-German collaboration leverages complementary expertise between the participating institutions. The German partner contributes strengths in empirical economics, blockchain applications and blockchain data mining, while the Taiwanese partner provides expertise in network analysis, machine learning, and knowledge graph creation.

• DFG Programme: Research Grants
• International Connection: Taiwan
• Partner Organisation: National Science and Technology Council (NSTC)
• Cooperation Partner: Professor Chih-Ping Wei, Ph.D.