PRISM: Partial-label Relational Inference with Spatial and Spectral Cues

In many real-world scenarios, precisely labeling graph data is costly or impractical, especially in domains like molecular biology or social networks, where annotation requires expert effort. This challenge motivates partial-label graph learning, where each graph is weakly annotated with a candidate label set containing the true label. However, such ambiguous supervision makes it hard to extract reliable semantics and increases the risk of overfitting to noisy candidates. To address these challenges, we propose PRISM, a unified framework that performs relational inference with spatial and spectral cues to resolve label ambiguity. PRISM captures discriminative spatial cues by aligning prototype-guided substructures across graphs and extracts global spectral cues by decomposing graph signals into multiple frequency bands with attention, preserving frequency-specific semantics. These complementary views are integrated into a hybrid relational graph, which supports confidence-aware label propagation under candidate constraints. A closed-loop refinement mechanism further stabilizes supervision via masked updates and momentum-based confidence estimation. Extensive experiments across diverse benchmarks demonstrate that PRISM consistently outperforms strong baselines under various noise settings, establishing a new paradigm for weakly supervised graph classification.