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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.
|Number of pages||12|
|Journal||IEEE Transactions on Computational Social Systems|
|Publication status||E-pub ahead of print - 24 Jan 2023|
Bibliographical noteThis work was supported in part by the Macao Science and Technology Development Fund under Macao Funding Scheme for Key Research and Development Projects under Grant 0025/2019/AKP; in part by the Department of Computer Science Startup Fund of Hong Kong Baptist University; in part by the Lam Woo Research Fund under Grant LWP20019; and in part by the Faculty
Research Grants of Lingnan University, Hong Kong, under Grant DB22B4.
- Analytical models
- Complex networks
- Data models
- Representation learning
- Task analysis
- complex networks
- graph neural networks (GNNs)
- graph representation
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- 1 Finished
Cluster-level Social Emotion Classification Across Domains
1/03/22 → 28/02/23
Project: Grant Research