S2Aligner decouples semantic and structural components in LLM-as-Aligner pre-training for sparse TAGs and uses structure-oriented reconstruction plus domain risk balancing to improve transferability and reduce generalization gaps.
Collective classification in network data.AI magazine, 29(3):93–93
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
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MLGIB derives variational bounds for multi-label message passing to maximize predictive information while constraining redundant noise from irrelevant labels.
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S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs
S2Aligner decouples semantic and structural components in LLM-as-Aligner pre-training for sparse TAGs and uses structure-oriented reconstruction plus domain risk balancing to improve transferability and reduce generalization gaps.
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MLGIB: Multi-Label Graph Information Bottleneck for Expressive and Robust Message Passing
MLGIB derives variational bounds for multi-label message passing to maximize predictive information while constraining redundant noise from irrelevant labels.