Graph-based Management and Mining of Ethereum Blockchain Data

Ethereum, currently the most actively-used and the second-largest blockchain platform, consists of a heterogeneous ecosystem, cohabited by human users, smart contracts (autonomous agents), ether (native cryptocurrency), tokens (digital assets), dApps (decentralized applications), and DeFi (decentralized finance). These key actors in the Ethereum interact with each other via transactions and contract calls. Given the highly connected structure, graph-based modeling is an optimal tool to analyze the data stored in Ethereum blockchain. Recently, several research works performed graph analysis on the publicly 

available Ethereum blockchain data to reveal insights into its transactions and for important downstream tasks, e.g., 

cryptocurrency price prediction, address clustering, phishing scams and counterfeit tokens detection.

 

In this talk, I shall discuss relevant literature on Ethereum blockchain data extraction and graphs construction, graph mining and machine learning methods used, target applications, and the new insights revealed by them, aiming towards providing a clear view of graph-data models for account-based blockchains such as the Ethereum