A large-scale standardized benchmark of GNN attacks and defenses reveals that target node selection and attacked-model training process can completely distort measured attack effectiveness.
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Linearized Graph Sequence Models recast graph message-passing as sequence modeling via separation of processing depth from propagation depth to integrate modern sequence advances while preserving graph inductive bias.
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Adversarial Graph Neural Network Benchmarks: Towards Practical and Fair Evaluation
A large-scale standardized benchmark of GNN attacks and defenses reveals that target node selection and attacked-model training process can completely distort measured attack effectiveness.
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From Message-Passing to Linearized Graph Sequence Models
Linearized Graph Sequence Models recast graph message-passing as sequence modeling via separation of processing depth from propagation depth to integrate modern sequence advances while preserving graph inductive bias.