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This paper proposes Clue2Group, a framework for clue-guided money laundering group discovery in financial networks, using a graph neural network to progressively recover criminal groups from initial clues.
This paper proposes a graph-driven real-time anti-money laundering monitoring framework (GCRMF) for cross-industry supply chain networks, leveraging heterogeneous graphs and temporal attention networks, achieving over 17.8% F1 improvement.