scholar.google.com › citations
Oct 5, 2024 · However, applying graph neural networks to fraud detection still faces challenges of capturing two types of fraudulent behaviors: the context- ...
Oct 11, 2024 · Semi-supervised node classification on graph-structured data has many applications such as fraud detection, fake account and review detection, ...
Semantic Scholar extracted view of "Graph neural network for fraud detection via context encoding and adaptive aggregation" by Chaoli Lou et al.
Jul 11, 2023 · In the applications of transaction fraud detection, the fact that fraudulent nodes are connected to legitimate ones by disguising the ...
Missing: context | Show results with:context
ASA-GNN: Adaptive Sampling and Aggregation-Based Graph Neural Network for Transaction Fraud Detection ... Transaction Fraud Detection via an Adaptive Graph Neural ...
Missing: encoding | Show results with:encoding
Jul 11, 2023 · Many machine learning methods have been proposed to achieve accurate transaction fraud detection, which is essential to the financial security ...
Missing: encoding | Show results with:encoding
Therefore, we propose a novel heterogeneous graph neural network called Spatial-Temporal-Aware Graph Transformer (STA-GT) for transaction fraud detection ...
Jul 11, 2023 · Fraud or not? Target node. Legitimate node. Fraudulent node. #𝑦. 𝑣 ...
Jul 17, 2024 · However, in most fraud detection cases, the suspected fraudulent data typically represents only a small fraction of the overall data. For ...
Duration: 1:01:29
Posted: Mar 29, 2023
Posted: Mar 29, 2023
Missing: context encoding adaptive aggregation.