Structural Identity Representation Learning for Blockchain-Enabled Metaverse Based on Complex Network Analysis

Bishenghui Tao, Hong-Ning Dai*, Haoran Xie, Fu Lee Wang

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

4 Citations (Scopus)


The metaverse and its underlying blockchain technology have attracted extensive attention in the past few years. How to mine, process, and analyze the tremendous data generated by the metaverse systems has posed a number of challenges. Aiming to address them, we mainly focus on modeling and understanding the blockchain transaction network from a structural identity perspective, which represents the entire network structure and reveals the relations among multiple entities. In this article, we analyze three metaverse-related systems: non-fungible token (NFT), Ethereum (ETH), and Bitcoin (BTC) from the structural-identity perspective. First, we conduct the complex network analysis of the metaverse network and obtain several new insights (i.e., power-law degree distribution, disconnection, disassortativity, preferential attachment, and non-rich-club effect). Secondly, based on such findings, we propose a novel representation learning method named structure-to-vector with random pace (SVRP) for learning both the latent representation and structural identity of the network. Thirdly, we conduct node classification and link prediction tasks with the integration of graph neural networks (GNNs). Empirical results on three real-world datasets demonstrate that our proposed SVRP outperforms other existing methods in multiple tasks. In particular, our SVRP achieves the highest node classification accuracy (Acc) (99.3 % ) and F 1-score (96.7 % ) while only requiring original non-attributed graphs.
Original languageEnglish
Pages (from-to)2214-2225
Number of pages12
JournalIEEE Transactions on Computational Social Systems
Issue number5
Early online date24 Jan 2023
Publication statusPublished - Oct 2023

Scopus Subject Areas

  • Modelling and Simulation
  • Social Sciences (miscellaneous)
  • Human-Computer Interaction

User-Defined Keywords

  • Blockchain
  • complex networks
  • graph neural networks (GNNs)
  • graph representation
  • metaverse


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