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Tianfu Wu

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#graph neural networks Preprint Aug 2026

ReCoG: Reciprocal Co-Evolution for Multimodal Graph Learning

ReCoG (Reciprocal Co-Evolution for Multimodal Graph Learning) is proposed, a new learning paradigm that tightly couples graph structure learning and multimodal representation learning through end-to-end reciprocal interaction and yields greater expressiveness than decoupled or two-stage formulations.

Rui Xue, Tianfu Wu · 0 citations