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Found 4 papers, 2 papers with code
Structural Alignment Improves Graph Test-Time Adaptation
Graph-based learning has achieved remarkable success in domains ranging from recommendation to fraud detection and particle physics by effectively capturing underlying interaction patterns.
A Graph Is More Than Its Nodes: Towards Structured Uncertainty-Aware Learning on Graphs
Current graph neural networks (GNNs) that tackle node classification on graphs tend to only focus on nodewise scores and are solely evaluated by nodewise metrics.
What Makes Graph Neural Networks Miscalibrated?
Furthermore, based on the insights from this study, we design a novel calibration method named Graph Attention Temperature Scaling (GATS), which is tailored for calibrating graph neural networks.
Automated Antenna Testing Using Encoder-Decoder-based Anomaly Detection
A contour-based anomaly detector can then map the reconstruction error matrix to an anomaly score to identify faulty antenna arrays and increase the classification F-measure (F-M) by up to 46%.
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