EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
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UNVERDICTED 3representative citing papers
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.
HIM learns hyperbolic user representations from propagation data to estimate influence strength and select seeds for model-agnostic influence maximization.
citing papers explorer
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Evaluating LLMs on Large-Scale Graph Property Estimation via Random Walks
EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
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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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Influence Strength Estimation in Hyperbolic Space for Social Influence Maximization
HIM learns hyperbolic user representations from propagation data to estimate influence strength and select seeds for model-agnostic influence maximization.