Zekai Chen

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.

Missing modalities in federated learning can be effectively synthesized, leading to substantial performance gains in multimodal tasks.

PRISM boosts performance in modality-deficient federated graph learning by intelligently retrieving and integrating missing modalities from the entire federation.