A multi-view forward positive and unlabeled graph learning method based on dictionary learning

With the increasing demand for graph data analysis in complex real-world scenarios, traditional graph classification methods that rely solely on labeled positive/negative samples face significant limitations due to data scarcity. To address this challenge, we propose a novel multi-view positive and unlabeled graph learning framework based on dictionary learning (MVPU-DL). Our approach innovatively utilizes unlabeled graphs as privileged information through three key mechanisms: 1) a multi-view dictionary learning paradigm with cross-view consistency constraints, which uses analytical dictionaries to generate discriminative sparse codes; 2) a novel PU-SVM classifier architecture that integrates view-specific dictionaries to enable robust feature representation from limited positive samples; 3) an alternating convex optimization strategy with provable convergence for jointly learning discriminative dictionaries and classification boundaries. Extensive experiments on 12 benchmark datasets spanning diverse domains—including biological compounds (PTC, MUTAG), chemical interactions (COX2, DHFR), and social networks (Twitter, DBLP)—validate the superior performance of MVPU-DL. The proposed cross-view dictionary alignment strategy is particularly effective under varying labeling ratios, achieving a significant average F1-score improvement of 2.16% (with a maximum improvement of 3.88%) compared to state-of-the-art baselines. These results demonstrate that MVPU-DL outperforms other methods with remarkable performance.