Review of blockchain application with Graph Neural Networks, Graph Convolutional Networks and Convolutional Neural Networks

TL;DR

The paper addresses the challenge of analyzing complex, graph-structured, and temporally dynamic blockchain data using advanced neural networks. It surveys how Graph Neural Networks (GNNs), Graph Convolutional Networks (GCNs), and Convolutional Neural Networks (CNNs), including their blockchain-adapted variants, can model blocks, transactions, and smart contracts across linear and DAG-based architectures. The contribution is a comprehensive overview of each model's capabilities, suitable applications (e.g., fraud detection, transaction verification, smart contract analysis, consensus optimization), and future research directions, providing a cohesive framework for leveraging deep learning in blockchain analytics. The work highlights the potential to improve security, scalability, and efficiency in decentralized networks, enabling more sophisticated analytics for next-generation blockchain systems.

Abstract

This paper reviews the applications of Graph Neural Networks (GNNs), Graph Convolutional Networks (GCNs), and Convolutional Neural Networks (CNNs) in blockchain technology. As the complexity and adoption of blockchain networks continue to grow, traditional analytical methods are proving inadequate in capturing the intricate relationships and dynamic behaviors of decentralized systems. To address these limitations, deep learning models such as GNNs, GCNs, and CNNs offer robust solutions by leveraging the unique graph-based and temporal structures inherent in blockchain architectures. GNNs and GCNs, in particular, excel in modeling the relational data of blockchain nodes and transactions, making them ideal for applications such as fraud detection, transaction verification, and smart contract analysis. Meanwhile, CNNs can be adapted to analyze blockchain data when represented as structured matrices, revealing hidden temporal and spatial patterns in transaction flows. This paper explores how these models enhance the efficiency, security, and scalability of both linear blockchains and Directed Acyclic Graph (DAG)-based systems, providing a comprehensive overview of their strengths and future research directions. By integrating advanced neural network techniques, we aim to demonstrate the potential of these models in revolutionizing blockchain analytics, paving the way for more sophisticated decentralized applications and improved network performance.