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An internal Madison Square Garden database tracked hundreds of celebrities with risk scores and labels including 'LGBTQIA' and 'DO NOT HOST', revealing extensive surveillance of VIPs and critics of owner Jim Dolan after a data breach by the ShinyHunters hacker group.
This paper proposes a graph neural network framework for financial fraud detection that integrates transaction records and identity information into node attributes, employs a multi-layer message passing mechanism, and uses weighted supervision and structural consistency regularization to improve risk scoring and probability calibration. Experiments on a public dataset show the method outperforms existing approaches.