Does Node-Level Out-of-Distribution Detection Need Stochastic Uncertainty?

Node-level out-of-distribution (OOD) detection is usually addressed with stochastic uncertainty methods, but the benchmarks that rank them have received little scrutiny. We show that the most widely reused protocols reward input scale and graph-level contrast rather than uncertainty. Standard Gaussian feature noise inflates feature norms on row-normalised graphs, and scoring a synthetic graph against the original tests graph discrimination instead of node detection. We propose a controlled protocol suite with scale-matched node-level corruptions, neighbourhood rewiring, held-out classes, and a real temporal shift, together with DCE, a deterministic encoder whose classifier reads each node's normalised features beside its aggregated context. Once scale is matched, sampling barely changes detection, whereas DCE outperforms dedicated uncertainty methods on feature anomalies in a single forward pass and achieves excellent accuracy. We also prove that node-wise decoders under separable losses cannot exploit a structured prior's cross-node correlation, and our experiments show no gain from such priors.