<ul><li>This paper introduces a knowledge distillation framework to integrate structural knowledge from Graph Neural Networks (GNNs) into Transformer models.</li><li>GNNs excel in capturing localized topological patterns, while Transformers are better at modeling long-range dependencies and global contextual information.</li><li>The proposed framework enables the transfer of multiscale structural knowledge, bridging the gap between GNNs and Transformers.</li><li>The approach establishes a new way of inheriting graph structural biases in Transformer architectures with wide-ranging applications.</li></ul>

Enhancing Transformer with GNN Structural Knowledge via Distillation: A Novel Approach

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