CANE estimates cluster-specific reliability of noisy LLM pseudo-labels on graphs without ground truth to improve label-free node classification.
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Learnable graph patches enable domain-agnostic pre-training of graph models by decomposing heterogeneous graphs into transferable semantic units via patch encoders and aggregators.
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Where LLM Annotators Fail: Label-Free Learning on Graphs with LLMs
CANE estimates cluster-specific reliability of noisy LLM pseudo-labels on graphs without ground truth to improve label-free node classification.
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Handling Feature Heterogeneity with Learnable Graph Patches
Learnable graph patches enable domain-agnostic pre-training of graph models by decomposing heterogeneous graphs into transferable semantic units via patch encoders and aggregators.