P. Lam

HeAD-CP: Heterophily-Aware Diffused Conformal Prediction Sets for Graph Neural Networks

HeAD-CP is proposed, a family of node-wise diffusion variants whose coefficients are determined by a label-free local-homophily estimate derived from the GNN softmax, which are most effective at extreme heterophily, intermediate heterophily, and moderate-to-high homophily, respectively, and all preserve the marginal coverage guarantee.