<ul><li>Cluster-wise Graph Transformer with Dual-granularity Kernelized Attention</li><li>The paper introduces the Node-to-Cluster Attention (N2C-Attn) mechanism for graph learning.</li><li>N2C-Attn incorporates techniques from Multiple Kernel Learning to capture information at both node and cluster levels.</li><li>The resulting architecture, Cluster-wise Graph Transformer (Cluster-GT), outperforms other methods on graph-level tasks.</li></ul>

Cluster-wise Graph Transformer with Dual-granularity Kernelized Attention

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