Experimental results under various attacks show that FRAUDAR outperforms the top competitor in accuracy of detecting both camouflaged and non-camouflaged fraud.
—Effectiveness: FRAUDAR outperforms state-of-the-art methods in detecting various fraud attacks in real-world graphs and detects a large amount of fraudulent ...
Accuracy of fraud detection on Amazon data in the experiment with “reverse camouflage” (edges from honest users to fraudulent products). (b) FRAUDAR has similar ...
—Effectiveness: FRAUDAR outperforms state-of-the-art methods in detecting various fraud attacks in real-world graphs and detects a large amount of fraudulent ...
HoloScope introduces a novel metric “contrast suspiciousness” integrating information from graph topology and spikes to more accurately detect fraudulent ...
Experimental results under various attacks show that FRAUDAR outperforms the top competitor in accuracy of detecting both camouflaged and non-camouflaged fraud.
Nov 7, 2024 · Recently, graph-based techniques have been adopted for financial fraud detection, leveraging graph topology to aggregate neighborhood ...
Sep 11, 2024 · Graph Neural Networks (GNNs) have been widely applied to fraud detection problems in recent years, revealing the suspiciousness of nodes by ...
Early Fraud Detection with Augmented Graph Learning · FRAUDAR: Bounding Graph Fraud in the Face of Camouflage · Error-Bounded Graph Anomaly Loss for GNNs ...
Nov 7, 2024 · This is essential in financial fraud detection for distinguishing between legitimate and fraudulent transactions. JA-GNN incorporates residual ...