GRAPH THEORY FOR BLOCKCHAIN: DIRECTION FOR FUTURE THROUGH BIBLIOMETRIX ANALYSIS

Abstract:

Graph theory is essential to blockchain technology, as all transactions can be viewed as edges between nodes, where nodes are addresses or blocks. Although blockchain and graph theory are becoming increasingly popular for research in security, scalability, and consensus mechanisms, the intersection of these two areas has yet to be mapped using comprehensive bibliometric analysis. This study investigates 1,250 peer-reviewed articles (2017–2025) from Scopus and systematically evaluates the literature on graph theory and blockchain to identify significant research trends, collaboration patterns, and areas of thematic clusters. Findings indicate that 70% of contributions are from China and the US, and there is little to no crossover between theoretical graph computing research and applied blockchain research. New methods (e.g., Graph Neural Networks) are emerging but have not yet been widely used in a blockchain context. The keyword analysis produced four thematic application areas for blockchain: consensus mechanisms, security and fraud

detection, scalability examples, and smart contract examples. This is the first comprehensive and systematic mapping of graph theory in blockchain, offers a comprehensive insight into the field, highlights research gaps that research can focus on, and provides insight for interdisciplinary research between mathematics and distributed systems in the future.