CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks
Credit card fraud poses a significant threat to the economy. While Graph Neural Network
(GNN)-based fraud detection methods perform well, they often overlook the causal effect of a …
(GNN)-based fraud detection methods perform well, they often overlook the causal effect of a …
A Survey on Self-Supervised Pre-Training of Graph Foundation Models: A Knowledge-Based Perspective
Graph self-supervised learning is now a go-to method for pre-training graph foundation
models, including graph neural networks, graph transformers, and more recent large …
models, including graph neural networks, graph transformers, and more recent large …
ProtoMix: Augmenting Health Status Representation Learning via Prototype-based Mixup
With the widespread adoption of electronic health records (EHR) data, deep learning
techniques have been broadly utilized for various health prediction tasks. Nevertheless, the …
techniques have been broadly utilized for various health prediction tasks. Nevertheless, the …
Infinite-horizon graph filters: Leveraging power series to enhance sparse information aggregation
Graph Neural Networks (GNNs) have shown considerable effectiveness in a variety of graph
learning tasks, particularly those based on the message-passing approach in recent years …
learning tasks, particularly those based on the message-passing approach in recent years …
Eatsa-Gnn: Edge-Aware and Two-Stage Attention for Enhancing Graph Neural Networks Based on Teacher-Student Mechanisms for Graph Node Classification
AJ Fofanah - Alpha Omar, Eatsa-Gnn: Edge-Aware and Two-Stage … - papers.ssrn.com
Abstract Graph Neural Networks (GNNs) have fundamentally transformed the way in which
we handle and examine data originating from non-Euclidean domains. Traditional …
we handle and examine data originating from non-Euclidean domains. Traditional …