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Beijing Institute of Technology
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OpenRTAG reveals that traditional GNNs struggle significantly more than LLM-GNNs under realistic data quality degradation, highlighting critical vulnerabilities in graph learning models.
FedLAB achieves up to 7.53% improvement over existing methods while ensuring that multimodal graph knowledge remains traceable and privacy-preserving.
Long-tailed distributions in federated graph learning can be effectively tackled with a dual decoupling approach that boosts minority node performance without compromising majority class accuracy.
Missing modalities in federated learning can be effectively synthesized, leading to substantial performance gains in multimodal tasks.
MAGE-RAG achieves a remarkable 52.75% accuracy on LongDocURL by intelligently balancing evidence coverage and context noise in long-document multimodal QA.
PRISM boosts performance in modality-deficient federated graph learning by intelligently retrieving and integrating missing modalities from the entire federation.
