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 three real-world P2E MMORPG datasets demonstrate that PU GNN achieves superior performances over previously suggested methods.

翻译：近年来，大型多人在线角色扮演游戏（MMORPGs）中的“玩赚”（P2E）系统使游戏内物品与现实世界价值的互换性空前增强。P2E MMORPGs中的物品可通过区块链网络直接与比特币、以太坊或Klaytn等加密货币进行兑换。与传统游戏内物品不同，一旦写入区块链，P2E物品即使遭遇支付欺诈、取消或退款等拒付欺诈行为，游戏运营团队也无法恢复。为解决此问题，我们提出了一种新颖的拒付欺诈预测方法——PU GNN，该方法利用带PU损失的图注意力网络，同时捕捉玩家的游戏内行为与P2E代币交易模式。通过采用改进的GraphSMOTE，所提模型应对了拒付欺诈数据集中标签分布的不平衡问题。在三个真实P2E MMORPG数据集上进行的实验表明，PU GNN在性能上优于此前提出的方法。