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Label Influence Propagation reveals that dynamically adjusting label influences can significantly enhance multi-label node classification performance, outperforming existing methods.
Learnable graph patches enable universal transferability across diverse datasets, significantly boosting downstream task performance.
HyGRAG achieves a 9.7% boost in multi-hop reasoning accuracy by seamlessly integrating contextual and relational knowledge from diverse sources.
Forget hand-crafted templates: DUET learns to generate user and item profiles jointly, boosting recommendation accuracy by better aligning textual representations.
