EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
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3 Pith papers cite this work, alongside 68 external citations. Polarity classification is still indexing.
3
Pith papers citing it
68
external citations · external index
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cs.LG 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
DisRFM uses polar Riemannian flow matching on constant-curvature manifolds to align graph domains while preserving label-relevant topology via radial Wasserstein and angular confidence matching.
S²PLR identifies a safe subspace for reliable pseudo-labels in source-free graph domain adaptation using semantic committee signals and structural contrastive verification, then applies noise-tolerant regularization to uncertain samples.
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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DisRFM: Polar Riemannian Flow Matching for Structure-Preserving Graph Domain Adaptation
DisRFM uses polar Riemannian flow matching on constant-curvature manifolds to align graph domains while preserving label-relevant topology via radial Wasserstein and angular confidence matching.
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Safe-Subspace Pseudo-Label Refinement for Source-Free Graph Domain Adaptation
S²PLR identifies a safe subspace for reliable pseudo-labels in source-free graph domain adaptation using semantic committee signals and structural contrastive verification, then applies noise-tolerant regularization to uncertain samples.