A Probabilistic Circuit Framework for Interpretable Graph PU Learning

Hamid, Sagad; Lee, Dohoo; Kong, Myeong; Braun, Tanya; Yoo, Jaemin

Zusammenfassung

How can we make graph positive-unlabeled (PU) learning interpretable? Existing methods jointly process node features and edge information, which obscures their interaction and makes predictions difficult to interpret. In this paper, we propose a novel interpretable graph PU learning framework that explicitly decouples feature and edge processing, enabling multi-level interpretability. Our framework first produces a core prediction from node features using probabilistic circuits (PCs) and then refines it using edge information, providing graph-level interpretability by exposing how graph structure affects predictions. For feature-based prediction, we construct two PCs through a careful split of the training nodes, yielding node-level interpretability by highlighting which nodes support the separation of positive and negative instances. Finally, by leveraging the tractability of PCs, we obtain feature-level interpretability via feature attribute marginalization, which quantifies attribute impact and importance while revealing interactions and dependencies. Experiments on 10 datasets show that our framework achieves strong performance while substantially improving interpretability. The code is available at https://github.com/hagad1/graphpu-cpu.