TY - JOUR
T1 - Structural Identity Representation Learning for Blockchain-Enabled Metaverse Based on Complex Network Analysis
AU - Tao, Bishenghui
AU - Dai, Hong Ning
AU - Xie, Haoran
AU - Wang, Fu Lee
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2023/10/1
Y1 - 2023/10/1
N2 - 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 F1-score (96.7%) while only requiring original non-attributed graphs.
AB - 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 F1-score (96.7%) while only requiring original non-attributed graphs.
KW - Blockchain
KW - complex networks
KW - graph neural networks (GNNs)
KW - graph representation
KW - metaverse
UR - http://www.scopus.com/inward/record.url?scp=85147305611&partnerID=8YFLogxK
U2 - 10.1109/TCSS.2022.3233059
DO - 10.1109/TCSS.2022.3233059
M3 - Article
AN - SCOPUS:85147305611
VL - 10
SP - 2214
EP - 2225
JO - IEEE Transactions on Computational Social Systems
JF - IEEE Transactions on Computational Social Systems
IS - 5
ER -