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Propagate, Then Sharpen: Post-Hoc Refinement of Frozen Node Classifiers
We study post-hoc refinement of frozen node classifiers: given only the graph $G$ and class distributions $Q$ predicted by a frozen model, can we improve accuracy without access to node features, model parameters, or gradients? APPNP answers this by propagating logits with a restart towards the init
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