针对区块链游戏生态的复杂性，提出一种基于时序有向加权网络的新型角色识别方法。该方法首先设计了节点投票算法ChainVoteRank以识别出关键基础角色，然后结合多特征融合的层次聚类算法挖掘潜在的隐蔽角色。以play-to-earn (P2E)模式区块链游戏AxieInfinity为对象进行研究，结果表明该P2E模式区块链游戏生态中存在6种基本角色：劳工、正常玩家、经理、繁育商、交易商和机构组织。相较于传统角色识别方法，该方法不仅可以更好地识别出区块链游戏生态中的主要用户角色，而且还揭示了P2E模式区块链游戏生态的角色演化过程、不同阶段中各角色发挥的作用，以及P2E生态日益严重的贫富差距。
Given the intricate nature of blockchain game ecosystems, this study proposes a new role identification method based on a time-series directed weighted network. Specifically, this method first designs a new node voting algorithm called ChainVoteRank to identify key basic roles, and then uncovers potential hidden roles by combining hierarchical clustering algorithm with multi-feature fusion. Focusing on Axie Infinity, a “play-to-earn” (P2E) mode blockchain game, the research findings indicate that the existence of six fundamental roles within the P2E mode blockchain game ecosystem: laborers, regular players, managers, breeders, traders, and institutional organizations. Compared with traditional methods, the proposed method can better identify the major user roles in the blockchain game ecosystem. Additionally, the study reveals the evolutionary process of roles within the P2E mode blockchain game ecosystem and the roles played by each role in different phases. Furthermore, a discussion of the escalating wealth disparity in P2E ecosystem is provided.
[1] Delic A J, Delfabbro P H. Profiling the potential risks and benefits of emerging “play to earn” games: a qualitative analysis of players’ experiences with Axie Infinity [J]. International Journal of Mental Health and Addiction, 2024, 22(1): 634-647.
[2] De Jesus S B, Austria D, Marcelo D R, et al. Play-to-earn: a qualitative analysis of the experiences and challenges faced by axie infinity online gamers amidst the COVID-19 pandemic [J]. International Journal of Psychology and Counseling, 2022, 1(12): 291-424.
[3] Alam O. Understanding the economies of blockchain games: an empirical analysis of Axie Infinity [D]. Zurich: Swiss Federal Institute of Technology in Zurich, 2022.
[4] Lai Y, Fan S, Cai W. Quantitative analysis of play-to-earn blockchain games: a case study of Axie Infinity [C]//IEEE International Conference on Metaverse Computing, Networking and Applications, 2023: 250-257.
[5] Jiang X J, Liu X F. Cryptokitties transaction network analysis: the rise and fall of the first blockchain game mania [J]. Frontiers in Physics, 2021, 9: 631-665.
[6] Francisco R, Rodelas N, Ubaldo J E. The perception of filipinos on the advent of cryptocurrency and non-fungible token (NFT) games [DB/OL]. 2022[2024-01-02]. https://arxiv.org/abs/2202.07467
[7] Nakamoto S. Bitcoin: a peer-to-peer electronic cash system [EB/OL]. 2008[2024-01-02]. https://bitcoin.org/en/bitcoin-paper
[8] Min T, Wang H, Guo Y, et al. Blockchain games: a survey [C]//IEEE Conference on Games, 2019: 1-8.
[9] Scholten O J, Hughes N G J, Deterding S, et al. Ethereum crypto-games: mechanics, prevalence, and gambling similarities [C]//Annual Symposium on Computer-Human Interaction in Play, 2019: 379-389.
[10] Brünjes L, Gabbay M J. UTxO-vs account-based smart contract blockchain programming paradigms [C]//9th International Symposium on Leveraging Applications of Formal Methods, 2020: 73-88.
[11] Härer F, Fill H G. Decentralized attestation of conceptual models using the ethereum blockchain [C]//21st IEEE Conference on Business Informatics, 2019, 1: 104-113.
[12] Khan A. Graph analysis of the ethereum blockchain data: a survey of datasets, methods, and future work [C]//IEEE International Conference on Blockchain, 2022: 250-257.
[13] Chen T, Zhang Y, Li Z, et al. TokenScope: automatically detecting inconsistent behaviors of cryptocurrency tokens in ethereum [C]//ACM SIGSAC Conference on Computer and Communications Security, 2019: 1503-1520.
[14] Kozen D C. Depth-first and breadth-first search [M]. New York: Springer, 1992: 19-24.
[15] Zhou S, Xu Z, Liu F. Method for determining the optimal number of clusters based on agglomerative hierarchical clustering [J]. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(12): 3007-3017.
[16] Aranganayagi S, Thangavel K. Clustering categorical data using silhouette coefficient as a relocating measure [C]//International Conference on Computational Intelligence and Multimedia Applications, 2007, 2: 13-17.
[17] Kim D, Oh A. How to find your friendly neighborhood: graph attention design with selfsupervision [DB/OL]. 2022[2024-01-02]. https://arxiv.org/abs/2204.04879.
[18] Brody S, Alon U, Yahav E. How attentive are graph attention networks? [DB/OL]. 2021[2024-01-02]. https://arxiv.org/abs/2105.14491.
[19] Brockschmidt M. GNN-FiLM: graph neural networks with feature-wise linear modulation [C]//International Conference on Machine Learning, 2020, 119: 1144-1152.
[20] Hartigan J A, Wong M A. Algorithm AS 136: A K-means clustering algorithm [J]. Journal of the Royal Statistical Society. Series C (Applied Statistics), 1979, 28(1): 100-108.
[21] Bhattacharjee P, Mitra P. A survey of density based clustering algorithms [J]. Frontiers of Computer Science, 2021, 15: 1-27.
[22] Henderson K, Gallagher B, Eliassi-rad T, et al. Rolx: structural role extraction & mining in large graphs [C]//18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012: 1231-1239.
