Dual-View Uncertainty Estimation with Noise Elimination for Positive-Unlabeled Graph Classification

Positive-unlabeled (PU) graph classification aims to learn graph-level classifiers from labeled positive graphs and mixed unlabeled graphs, where only known active compounds are labeled. Due to limited labeled graphs, existing PU graph classification methods mainly focus on pseudo-label generation. However, relying on single prediction confidence may amplify confirmation bias under overconfident mispredictions, leading to noisy graph representations and degraded generalization. To address these issues, we propose Dual-view Uncertainty Estimation with Noise Elimination (called DUNE), which jointly refines pseudo-label reliability and suppresses noisy supervision for robust PU graph learning. Specifically, DUNE first introduces a dual-view uncertainty estimation strategy that evaluates unlabeled graphs from both intra-graph structural consistency and inter-graph distribution discrepancy. It generates perturbed graph views and employs a Pareto-guided selection to obtain reliable pseudo-labels, while constructing a graph-of-graphs structure to capture inter-graph dependencies and calibrate confidence. Furthermore, DUNE employs dynamic noise elimination, where confident pseudo-labeled graphs are re-evaluated according to their representation deviation from class prototypes during later training to remove harmful mislabeled graphs. Extensive experiments on multiple public graph classification benchmarks demonstrate that DUNE consistently outperforms state-of-the-art PU graph classification baselines.