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Analysis Of Core-periphery Structure Based On Clustering Aggregation In The Nft Transfer Network.

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Analysis Of Core-periphery Structure Based On Clustering Aggregation In The Nft Transfer Network.

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Analysis of Core-Periphery Structure Based on Clustering Aggregation in the NFT Transfer Network.

Zijuan Chen¹, Jianyong Yu¹, Yulong Wang¹

¹School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411100, China.

Entropy (Basel, Switzerland)

|April 26, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Researchers analyzed the non-fungible token (NFT) transfer network, identifying core nodes using Bayesian and stochastic block models. Their novel method improves structural representation by 6-10%, offering insights into the NFT market.

Keywords:

Ethereum NFT cluster aggregation core–periphery structure network structure random block model

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Area of Science:

Blockchain Technology
Network Science
Digital Assets

Background:

Non-fungible tokens (NFTs) are a new digital asset class on blockchain platforms like Ethereum.
NFT transfer networks display complex core-periphery structures with variations based on partitioning methods.
Understanding these network structures is crucial for analyzing the NFT market.

Purpose of the Study:

To develop a robust method for characterizing core-periphery structures in NFT transfer networks.
To improve the accuracy and representativeness of identified network structures.
To provide a theoretical foundation for further NFT market analysis.

Main Methods:

Proposed a Bayesian and stochastic block model (SBM) based method for core-periphery structure characterization.

Incorporated prior knowledge to enhance the fit of SBMs.

Introduced a locally weighted core-periphery structure aggregation (LWCSA) scheme using the minimum description length (MDL) principle.

Main Results:

Identified core nodes as a small fraction (2.3-5%) of the total nodes in the NFT transfer network.
Achieved a 6-10% improvement in the normalized mutual information (NMI) index compared to baseline methods.
Demonstrated enhanced structural representation and accuracy of the proposed method.

Conclusions:

The developed method accurately characterizes NFT network structures, revealing a distinct core-periphery organization.
The findings provide valuable insights into the dynamics of the NFT market.
This research lays the groundwork for advanced network analysis within the digital asset space.