Analysis of Information Propagation in Ethereum Network Using Combined
  Graph Attention Network and Reinforcement Learning to Optimize Network
  Efficiency and Scalability
Stefan Kambiz Behfar Jon Crowcroft
Blockchain technology has revolutionized the way information is propagated in
decentralized networks. Ethereum plays a pivotal role in facilitating smart
contracts and decentralized applications. Understanding information propagation
dynamics in Ethereum is crucial for ensuring network efficiency, security, and
scalability. In this study, we propose an innovative approach that utilizes
Graph Convolutional Networks (GCNs) to analyze the information propagation
patterns in the Ethereum network. The first phase of our research involves data
collection from the Ethereum blockchain, consisting of blocks, transactions,
and node degrees. We construct a transaction graph representation using
adjacency matrices to capture the node embeddings; while our major contribution
is to develop a combined Graph Attention Network (GAT) and Reinforcement
Learning (RL) model to optimize the network efficiency and scalability. It
learns the best actions to take in various network states, ultimately leading
to improved network efficiency, throughput, and optimize gas limits for block
processing. In the experimental evaluation, we analyze the performance of our
model on a large-scale Ethereum dataset. We investigate effectively aggregating
information from neighboring nodes capturing graph structure and updating node
embeddings using GCN with the objective of transaction pattern prediction,
accounting for varying network loads and number of blocks. Not only we design a
gas limit optimization model and provide the algorithm, but also to address
scalability, we demonstrate the use and implementation of sparse matrices in
GraphConv, GraphSAGE, and GAT. The results indicate that our designed GAT-RL
model achieves superior results compared to other GCN models in terms of
performance. It effectively propagates information across the network,
optimizing gas limits for block processing and improving network efficiency.