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From Phase Transition to Systemic Failure: A Decoupled Analytics Framework for GNN Robustness
Data quality is a major bottleneck for the reliable deployment of graph neural networks (GNNs) in real-world graph mining tasks. Among various sources of degradation, label noise and feature distribution shift (hereafter referred to as distribution shift) are two common yet fundamentally different c
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