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Found 3 papers, 2 papers with code
GAT-COBO: Cost-Sensitive Graph Neural Network for Telecom Fraud Detection
Extensive experiments on two real-world telecom fraud detection datasets demonstrate that our proposed method is effective for the graph imbalance problem, outperforming the state-of-the-art GNNs and GNN-based fraud detectors.
Cost Sensitive GNN-based Imbalanced Learning for Mobile Social Network Fraud Detection
But the imbalance problem in the aforementioned data, which could severely hinder the effectiveness of fraud detectors based on graph neural networks(GNN), has hardly been addressed in previous work.
TSAM: Temporal Link Prediction in Directed Networks based on Self-Attention Mechanism
In this paper, we address the problem of temporal link prediction in directed networks and propose a deep learning model based on GCN and self-attention mechanism, namely TSAM.
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