The increasing adoption of tokens on the Ethereum blockchain has given rise to many distinct economic communities whose activity history is publicly accessible. In this paper we study the communities of Ethereum fungible and non-fungible tokens, regulated, respectively, by the ERC-20 and ERC-721 standards. In particular, we focus on token transfers and consider the top 100 largest ERC-20 and ERC-721 ecosystems by number of transfers, modeling them as networks where nodes correspond to participants and edges represent token transfers. We analyze their main topological properties and conduct a clustering-based study to identify groups of graphs with similar topologies. Subsequently, we classify the networks based on the application domain of their corresponding token and investigate whether graphs with similar topologies correspond to tokens within the same domain. We also conduct a temporal analysis of token popularity based on the historical transfer activity. Our findings highlight the existence of common topological properties (e.g., absence of small world effect) across both types of tokens. In contrast, the clustering analysis indicates no evident connection between the token application domain and the structure of the induced transfer networks, with the exception of non-fungible tokens associated with spamming activities.
Comparing Ethereum fungible and non-fungible tokens: an analysis of transfer networks
Loporchio, Matteo;Di Francesco Maesa, Damiano;Bernasconi, Anna;Ricci, Laura
2024-01-01
Abstract
The increasing adoption of tokens on the Ethereum blockchain has given rise to many distinct economic communities whose activity history is publicly accessible. In this paper we study the communities of Ethereum fungible and non-fungible tokens, regulated, respectively, by the ERC-20 and ERC-721 standards. In particular, we focus on token transfers and consider the top 100 largest ERC-20 and ERC-721 ecosystems by number of transfers, modeling them as networks where nodes correspond to participants and edges represent token transfers. We analyze their main topological properties and conduct a clustering-based study to identify groups of graphs with similar topologies. Subsequently, we classify the networks based on the application domain of their corresponding token and investigate whether graphs with similar topologies correspond to tokens within the same domain. We also conduct a temporal analysis of token popularity based on the historical transfer activity. Our findings highlight the existence of common topological properties (e.g., absence of small world effect) across both types of tokens. In contrast, the clustering analysis indicates no evident connection between the token application domain and the structure of the induced transfer networks, with the exception of non-fungible tokens associated with spamming activities.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.