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 to efficiently implement the formulation. HL-GNN adopts intra-layer propagation and inter-layer connections%2C allowing it to reach a depth of around 20 layers with lower time complexity than GCN. Extensive experiments on the Planetoid%2C Amazon%2C and OGB datasets underscore the effectiveness and efficiency of HL-GNN. It outperforms existing methods by a large margin in prediction performance. Additionally%2C HL-GNN is several orders of magnitude faster than heuristic-inspired methods while requiring only a few trainable parameters. The case study further demonstrates that the generalized heuristics and learned weights are highly interpretable.?quality=80&w=800)
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