research-article

PU-Detector: A PU Learning-based Framework for Real Money Trading Detection in MMORPG

Authors:

Gang PanAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data, Volume 18, Issue 4

Article No.: 91, Pages 1 - 26

https://doi.org/10.1145/3638561

Published: 13 February 2024 Publication History

Abstract

Massive multiplayer online role-playing games (MMORPG) have been becoming one of the most popular and exciting online games. In recent years, a cheating phenomenon called real money trading (RMT) has arisen and damaged the fantasy world in many ways. RMT is the sale of in-game items, currency, or even characters to earn real money, breaking the balance of the game economy ecosystem and damaging the game experience. Therefore, some studies have emerged to address the problem of RMT detection. However, they cannot well handle the label uncertainty problem in practice, where there are only labeled RMT samples (positive samples) and unlabeled samples, which could either be RMT samples or normal transactions (negative samples). Meanwhile, the trading relationship between RMTers is modeled in a simple way, leading to some normal transactions being falsely classified as RMT. In this article, we propose PU-Detector, a novel framework based on PU learning (learning from positive and unlabeled data) for RMT detection, considering the fact that there are only labeled RMT samples and other unlabeled transactions. We first automatically estimate the likelihood of one transaction being RMT by developing an improved PU learning method and proposing an assessment rule. Sequentially, we use the estimated likelihood as edge weight to construct a trading graph to learn trader representation. Then, with the trader representations and basic trading features, we detect RMT samples by the improved PU learning method. PU-Detector is evaluated on a large-scale real world dataset consisting of 33,809,956 transaction logs generated by 43,217 unique players. Compared with other approaches, it achieves the state-of-the-art performance and demonstrates its advantages in detecting underlying RMT samples.

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Cited By

Yang HHu JChen JHu SGeng JZhu QHuang T(2025)MAHKT: Knowledge tracing with multi-association heterogeneous graph embedding based on knowledge transferKnowledge-Based Systems10.1016/j.knosys.2025.112958310(112958)Online publication date: Feb-2025
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Zhao SYuan XWu RHu ZLiu HWang KHu YLv TFan CTong XHan JZheng YHao JChua TNgo CKumar RLauw HKa-Wei Lee R(2024)The MMO Economist: AI Empowers Robust, Healthy, and Sustainable P2W MMO EconomiesCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3648344(443-452)Online publication date: 13-May-2024
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Duan ZGu HKe YZhou D(2024)EBERT: A lightweight expression-enhanced large-scale pre-trained language model for mathematics educationKnowledge-Based Systems10.1016/j.knosys.2024.112118300(112118)Online publication date: Sep-2024
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Index Terms

PU-Detector: A PU Learning-based Framework for Real Money Trading Detection in MMORPG
1. Computing methodologies
  1. Artificial intelligence
2. Human-centered computing
  1. Ubiquitous and mobile computing

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cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 18, Issue 4

May 2024

707 pages

EISSN:1556-472X

DOI:10.1145/3613622

Editor:
Jian Pei
Duke University, USA

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 February 2024

Online AM: 29 December 2023

Accepted: 15 December 2023

Revised: 30 September 2023

Received: 10 October 2022

Published in TKDD Volume 18, Issue 4

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STI 2030 Major Projects
Natural Science Foundation of China
Key Research and Development Program of Zhejiang Province

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Cited By

Yang HHu JChen JHu SGeng JZhu QHuang T(2025)MAHKT: Knowledge tracing with multi-association heterogeneous graph embedding based on knowledge transferKnowledge-Based Systems10.1016/j.knosys.2025.112958310(112958)Online publication date: Feb-2025
https://doi.org/10.1016/j.knosys.2025.112958
Zhao SYuan XWu RHu ZLiu HWang KHu YLv TFan CTong XHan JZheng YHao JChua TNgo CKumar RLauw HKa-Wei Lee R(2024)The MMO Economist: AI Empowers Robust, Healthy, and Sustainable P2W MMO EconomiesCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3648344(443-452)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3648344
Duan ZGu HKe YZhou D(2024)EBERT: A lightweight expression-enhanced large-scale pre-trained language model for mathematics educationKnowledge-Based Systems10.1016/j.knosys.2024.112118300(112118)Online publication date: Sep-2024
https://doi.org/10.1016/j.knosys.2024.112118
Xu PNing ZXiao MFeng GLi XZhou YWang P(2024)scCDCG: Efficient Deep Structural Clustering for Single-Cell RNA-Seq via Deep Cut-Informed Graph EmbeddingDatabase Systems for Advanced Applications10.1007/978-981-97-5575-2_11(172-187)Online publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1007/978-981-97-5575-2_11
Zhang YMa HZhang DShu KLi X(2024)Graph Contrastive Learning for Dissolved Gas AnalysisAdvanced Data Mining and Applications10.1007/978-981-96-0821-8_12(178-190)Online publication date: 3-Dec-2024
https://dl.acm.org/doi/10.1007/978-981-96-0821-8_12

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