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This paper identifies that in LayerNorm-based GNNs, positive per-node scalars like node degree are erased when placed before LayerNorm but survive after LayerNorm. The authors propose PostDeg, a parameter-free post-LayerNorm inverse-degree scale, achieving significant gains on influence maximization, network dismantling, and maximum independent set tasks.