Abstract: Graph Neural Networks excel at node classification but falter on real-world imbalanced graphs, leading to critical performance drops in high-stakes applications like fraud detection, rare ...
Abstract: In recent years, Graph Neural Networks (GNNs) have achieved significant success in graph-based tasks. However, they still face challenges in complex scenarios, particularly in integrating ...
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