The recent advent of play-to-earn (P2E) systems in massively multiplayer online role-playing games (MMORPGs) has made in-game goods interchangeable with real-world values more than ever before. The goods in the P2E MMORPGs can be directly exchanged with cryptocurrencies such as Bitcoin, Ethereum, or Klaytn via blockchain networks. Unlike traditional in-game goods, once they had been written to the blockchains, P2E goods cannot be restored by the game operation teams even with chargeback fraud such as payment fraud, cancellation, or refund. To tackle the problem, we propose a novel chargeback fraud prediction method, PU GNN, which leverages graph attention networks with PU loss to capture both the players' in-game behavior with P2E token transaction patterns. With the adoption of modified GraphSMOTE, the proposed model handles the imbalanced distribution of labels in chargeback fraud datasets. The conducted experiments on two real-world P2E MMORPG datasets demonstrate that PU GNN achieves superior performances over previously suggested methods.

翻译：在大规模多人在线角色扮演游戏(MMMORPGs)中,最近出现了玩到手的游戏系统(P2E),使游戏中的商品比以往任何时候更能与现实世界价值互换。P2E MMOPGs中的商品可以直接与Bitcoin、Etheum或Klaytn等加密系统进行交换。与传统的游戏中商品不同,P2E货物一旦被写到块链中,即使游戏操作团队进行收费欺诈,例如付款欺诈、取消或退款,也无法恢复。为了解决这个问题,我们提出了一个新的收费欺诈预测方法,即PUGNNN,它利用图形关注网络的PU损失来利用P2E象征性交易模式来捕捉两个玩家的游戏中行为。随着采用修改的图示SMOTE,拟议的模型处理充电欺诈数据集中标签分布不平衡的问题。在两个真实世界的 P2E MMOPG数据集上进行的实验表明,PU GNN在先前建议的方法上取得了更高的业绩。