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.
LIU Kai, WANG Jiaxin, MAO Qian'ang, CHEN Yufei, YAN Jiaqi
. Dynamic Role Identification and Evolutionary Analysis of Blockchain Game Ecosystems: A Case Study of Axie Infinity[J]. Journal of Applied Sciences, 2024
, 42(4)
: 642
-658
.
DOI: 10.3969/j.issn.0255-8297.2024.04.007
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