Efficient and Exact Global Attention on Latent Summaries for Knowledge Graph Reasoning
This paper introduces LaGR, a novel approach for integrating global information in knowledge graph reasoning that compresses the graph into a fixed, compact set of latent summaries and applies exact self-attention within this latent space, resulting in scale-invariant attention and stable performance across diverse data settings.
Chen Lin, Lei Wang, Yin Zhang et al.
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