Both the metaverse and its underlying blockchain technology have attracted extensive attention in the past few years. It becomes a natural problem to extract, process, and analyze the tremendous data generated by the blockchain systems for various metaverse applications though it also poses diverse challenges. Amongst those challenges, this paper mainly focuses 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 paper, we propose a novel representation learning method named Structure-to-Vector with Random Pace (SVRP) for learning both latent representation and structural identity of blockchain transaction networks. We then conduct node classification and link prediction tasks with integration with Graph Neural Networks (GNNs). Empirical results on three representative blockchain data sets, namely Non-fungible token (NFT), Ethereum (ETH), and Bitcoin (BTC), demonstrate that our proposed SVRP outperforms other existing methods in multiple tasks. In particular, our SVRP achieves the highest node classification accuracy (99.3%) while only requiring original non-attributed graphs (i.e., graphs without node features).
|Title of host publication||2022 IEEE 24th International Workshop on Multimedia Signal Processing, MMSP 2022|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||E-pub ahead of print - 22 Nov 2022|
|Event||24th IEEE International Workshop on Multimedia Signal Processing, MMSP 2022 - Shanghai, China|
Duration: 26 Sep 2022 → 28 Sep 2022
|Name||2022 IEEE 24th International Workshop on Multimedia Signal Processing, MMSP 2022|
|Conference||24th IEEE International Workshop on Multimedia Signal Processing, MMSP 2022|
|Period||26/09/22 → 28/09/22|
Bibliographical noteFunding Information:
The research has been supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS16/E01/19), CS Department Start-up Fund (179432) of Hong Kong Baptist University, and the Faculty Research Grants (DB22B7) of Lingnan University, Hong Kong.
© 2022 IEEE.
- Complex Networks
- Graph Neural Networks
- Graph Representation