GraphBlock-Mine: A GNN-Enhanced Blockchain Model for Mining Complex Relational Patterns in Decentralized Graph Structures

Abstract

The explosive increase in decentralized data on blockchain systems has opened up new possibilities and problems of mining complex relational patterns in trustless, distributed systems. Conventional data mining models fall short of describing the complex relationships and heterogeneity of data stored in a blockchain, and more so when the data is distributed in graph form. This paper suggests GraphBlock-Mine, a new system that combines Graph Neural Networks (GNNs) with blockchain to support secure, scalable, and intelligent mining of patterns on decentralized graph-based data. The framework also can exploit the representational capacity of GNNs to capture dynamic relationship among nodes, temporal relationships, and structural anomalies with immutability and provenance via smart contracts and consensus mechanisms. GraphBlock-Mine uses an off-chain computation approach, which is reviewed using verifiable proofs on-chain to assure data integrity and privacy. We test the offered model on simulated blockchain networks and decentralized databases in real conditions, which proves the high level of pattern recognition accuracy, fault tolerance, and security with regard to traditional mining methods. The paper lays the foundations of the next-generation blockchain intelligence systems that can discover knowledge decentralized and in real-time across different fields, such as finance, supply chain, and social networks.