UiT The Arctic University of Norway

When sensor failures spread along graph edges and value propagation loses its footing, PEMix conditions the distribution of missing node features on global structural representations, raising TADPOLE macro-F1 curve area from 0.7469 to 0.8243.

When sensor failures spread along graph edges and value propagation loses its footing, PEMix conditions the distribution of missing node features on global structural representations, raising TADPOLE macro-F1 curve area from 0.7469 to 0.8243.

The authors propose Polytopal Neural Networks (PNNs), which constrain each layer's latent representation during training to a convex combination of learned archetypes so that the explanatory coordinates are the same coordinates the next layer receives; across image classification and reconstruction benchmarks, PNNs approach unconstrained networks with small accuracy loss, outperform VQ-VAE and Dirichlet VAE in unsupervised reconstruction, and offer a VQ training route without EMA updates, commitment loss, or straight-through estimator.

The authors propose Polytopal Neural Networks (PNNs), which constrain each layer's latent representation during training to a convex combination of learned archetypes so that the explanatory coordinates are the same coordinates the next layer receives; across image classification and reconstruction benchmarks, PNNs approach unconstrained networks with small accuracy loss, outperform VQ-VAE and Dirichlet VAE in unsupervised reconstruction, and offer a VQ training route without EMA updates, commitment loss, or straight-through estimator.

The work introduces a first statistical framework for epistemic uncertainty in node classification, comprising an information-growth experimental protocol and a consistency criterion, and uses projective graph data-generating processes to ensure nested graphs are coherent observations of the same process; the analysis shows that graph evidential deep learning (EDL) methods retain non-vanishing epistemic uncertainty at the population optimum and regulate epistemic and aleatoric uncertainty externally through hyperparameters, thus failing consistency, whereas graph bootstrap ensembles (GB-Ens) capture both data and procedural uncertainty through graph resampling and randomized training and exhibit epistemic uncertainty contraction beyond standard deep ensembles under the same protocol.

The work introduces a first statistical framework for epistemic uncertainty in node classification, comprising an information-growth experimental protocol and a consistency criterion, and uses projective graph data-generating processes to ensure nested graphs are coherent observations of the same process; the analysis shows that graph evidential deep learning (EDL) methods retain non-vanishing epistemic uncertainty at the population optimum and regulate epistemic and aleatoric uncertainty externally through hyperparameters, thus failing consistency, whereas graph bootstrap ensembles (GB-Ens) capture both data and procedural uncertainty through graph resampling and randomized training and exhibit epistemic uncertainty contraction beyond standard deep ensembles under the same protocol.