Finding Influential Nodes in Ethereum Using Machine Learning

Abstract

Ethereum blockchain is the market leading platform for decentralized applications and smart contracts that have powered the new age of financial ecosystem. In order to improve security and performance, identify influential nodes, and understand network dynamics on Ethereum it is critical to identify influential nodes in Ethereum. In this thesis work, we explore machine learning techniques for discovery of these nodes using graph based algorithms, centrality measures and clustering methods. It studies the impact of a node in terms of frequency of usage, connectivity and computational power for a node. Finally, we compare performance of our proposed methodology combining supervised learning and graph neural networks to their traditional counterparts and demonstrate our approach outperforms existing methods. We demonstrate that highly influential nodes engage in unique patterns of behavior, which are detectable and categorizable. We contribute to understanding of the network structure of Ethereum, along with a scalable approach to monitoring and optimising blockchain ecosystems. Moreover we discuss the implications for network robustness, fraud detection and protocol enhancements, and demonstrate the promise of machine learning for blockchain analytics.

Keywords: Ethereum, Blockchain, Machine Learning, Influential Nodes, Graph Neural Networks, Decentralized Networks