论文标题
PU GNN：P2E MMORPG中的拒绝欺诈检测通过图形注意网络不平衡PU标签
PU GNN: Chargeback Fraud Detection in P2E MMORPGs via Graph Attention Networks with Imbalanced PU Labels
论文作者
论文摘要
在大规模多人在线角色扮演游戏（MMORPG）中，Play-to-earn（P2E）系统最近出现了，使游戏中的商品比以往任何时候都可以与现实世界值更具互换。 P2E MMORPG中的商品可以通过区块链网络直接与比特币，以太坊或Klaytn等加密货币交换。与传统的游戏中的商品（一旦将它们写给区块链），即使使用付款欺诈，取消或退款等退款欺诈，游戏运营团队也无法恢复P2E商品。为了解决这个问题，我们提出了一种新颖的拒绝欺诈预测方法PU GNN，该方法利用PU损失的图形注意力网络来捕获玩家具有P2E代币交易模式的游戏中的行为。通过采用修改的图形量，所提出的模型处理了退款欺诈数据集中标签的不平衡分布。在三个现实世界P2E MMORPG数据集上进行的实验表明，PU GNN比先前建议的方法具有出色的性能。
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.
