GNSN adds convection governed by a dynamic velocity field to graph message passing, adaptively balancing it with diffusion to handle varying homophily levels and reduce oversmoothing while outperforming baselines on 12 datasets.
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DCGL introduces a dual-channel architecture with multi-level contrastive learning and frequency-adaptive fusion to improve knowledge-aware recommendations, especially in sparse data settings.
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Graph Navier Stokes Networks
GNSN adds convection governed by a dynamic velocity field to graph message passing, adaptively balancing it with diffusion to handle varying homophily levels and reduce oversmoothing while outperforming baselines on 12 datasets.
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DCGL: Dual-Channel Graph Learning with Large Language Models for Knowledge-Aware Recommendation
DCGL introduces a dual-channel architecture with multi-level contrastive learning and frequency-adaptive fusion to improve knowledge-aware recommendations, especially in sparse data settings.