From Phase Transition to Systemic Failure: A Decoupled Analytics Framework for GNN Robustness

arXiv:2609.31656 · cs.LG, cs.AI, cs.IR · Submitted 2026-09-15 · Read on arXiv

cs.LG, cs.AI, cs.IR

Submitted: 2026-09-15

Updated: 2026-09-15

Terminology

• Distribution Shift — 23× in this paper · explained in 5 episodes
• Benchmark — 16× in this paper · explained in 2 episodes
• Robustness — 13× in this paper · explained in 5 episodes
• GNNs — 9× in this paper · explained in 2 episodes
• Generalization — 8× in this paper · explained in 13 episodes
• Calibration — 4× in this paper · explained in 7 episodes
• Aggregation — 3× in this paper · explained in 1 episode
• Failure Modes — 3× in this paper · explained in 3 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Decoupling — 2× in this paper · explained in 2 episodes
• Domain Shift — 2× in this paper · explained in 4 episodes
• Graph Convolutional Networks — 2× in this paper
• Graph Neural Networks (GNNs) — 2× in this paper · explained in 7 episodes
• Mean Squared Error — 2× in this paper
• Regularization — 2× in this paper · explained in 3 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Adversarial Robustness — 1× in this paper · explained in 3 episodes
• Annotation Errors — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Over-smoothing — 1× in this paper · explained in 4 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Revisiting Graph Neural Networks: All We Have is Low-Pass Filters

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