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Blockchain technology has revolutionized the way information is propagated indecentralized networks. Ethereum plays a pivotal role in facilitating smartcontracts and decentralized applications. Understanding information propagationdynamics in Ethereum is crucial for ensuring network efficiency, security, andscalability. In this study, we propose an innovative approach that utilizesGraph Convolutional Networks (GCNs) to analyze the information propagationpatterns in the Ethereum network. The first phase of our research involves datacollection from the Ethereum blockchain, consisting of blocks, transactions,and node degrees. We construct a transaction graph representation usingadjacency matrices to capture the node embeddings; while our major contributionis to develop a combined Graph Attention Network (GAT) and ReinforcementLearning (RL) model to optimize the network efficiency and scalability. Itlearns the best actions to take in various network states, ultimately leadingto improved network efficiency, throughput, and optimize gas limits for blockprocessing. In the experimental evaluation, we analyze the performance of ourmodel on a large-scale Ethereum dataset. We investigate effectively aggregatinginformation from neighboring nodes capturing graph structure and updating nodeembeddings using GCN with the objective of transaction pattern prediction,accounting for varying network loads and number of blocks. Not only we design agas limit optimization model and provide the algorithm, but also to addressscalability, we demonstrate the use and implementation of sparse matrices inGraphConv, GraphSAGE, and GAT. The results indicate that our designed GAT-RLmodel achieves superior results compared to other GCN models in terms ofperformance. It effectively propagates information across the network,optimizing gas limits for block processing and improving network efficiency.

Analysis of Information Propagation in Ethereum Network Using Combined  Graph Attention Network and Reinforcement Learning to Optimize Network  Efficiency and Scalability