Sample, Don't Search: One-Run Ensembles for Node Property Prediction

Node property prediction often requires tuning graph propagation and model choices for each dataset. We introduce GraphPack, which explores these choices through graph-view sampling and ensemble selection in a single training run. A shared bank of propagated features and training-label views supplies different inputs to randomly configured MLPs. These MLPs train together as one packed module on a single GPU. Validation-based selection combines their snapshots with gradient-boosted trees, adapting the model and view mix to each graph under fixed sampling rules. On ten GraphLand datasets, GraphPack achieves the best or tied performance on seven against tree-based, tuned decoupled, and tuned message-passing pipelines with access to the same inputs. It also achieves higher mean scores on all ten datasets than an ensemble of the best configuration from a 50-trial SIGN search. That search-and-ensemble workflow takes 9.1 times the wall-clock time of GraphPack, measured by the geometric mean across datasets. Controlled ablations across six datasets show that distributing graph views across members improves over giving every member all views. These results identify graph-view diversity as a source of ensemble gains and support sampling and selection as a practical alternative to per-dataset tuning.