mlsanityData to 14 Oct 2026

Fraud Detection in Cryptocurrency Markets with Spatio-Temporal Graph Neural Networks

arXiv 2604.24590 ↗First posted 27 Apr 2026cs.LG cs.CEcited by 0

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Abstract (arXiv)

Technological advancements in cryptocurrency markets have increased accessibility for investors, but concurrently exposed them to the risks of market manipulations. Existing fraud detection mechanisms typically rely on machine learning methods that treat each financial asset (i.e., token) and its related transactions independently. However, market manipulation strategies are rarely isolated events, but are rather characterized by coordination, repetition, and frequent transfers among related assets. This suggests that relational structure constitutes an integral component of the signal and can be effectively represented through graphical means. In this paper, we propose three graph construction methods that rely on aggregated hourly market data. The proposed graphs are processed by a unified spatio-temporal Graph Neural Network (GNN) architecture that combines attention-based spatial aggregation with temporal Transformer encoding. We evaluate our methodology on a real-world dataset comprised of pump-and-dump schemes in cryptocurrency markets, spanning a period of over three years. Our comparative results showcase that our graph-based models achieve significant improvements over standard machine learning baselines in detecting anomalous events. Our work highlights that learned market connectivity provides substantial gains for detecting coordinated market manipulation schemes.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 37 references that are in the map. First posted on arXiv 27 Apr 2026.

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References · 18

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6 more
  1. 2021Discrete Graph Structure Learning for Forecasting Multiple Time Seriescited by 171
  2. 2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Surveycited by 140
  3. 2019Exploring Graph Neural Networks for Stock Market Predictions with Rolling Window Analysiscited by 27
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  5. 2020Pump and Dumps in the Bitcoin Era: Real Time Detection of Cryptocurrency Market Manipulationscited by 7
  6. 2023Anomaly Detection in Global Financial Markets with Graph Neural Networks and Nonextensive Entropycited by 1

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