Fig. 1. Dynamic Positive–Unlabeled (DPU) framework for predicting missing mammal–virus associations.

The DPU framework combines a classifier and a propensity score model to jointly account for both the ground truth mechanism, responsible for determining truly existent associations from nonexistent ones, and the labeling mechanism, responsible for which existent associations are effectively documented through scientific evidence. Specifically, the classifier employs a graph neural network (GNN) framework to capture emerging patterns within the observed network of mammal–virus associations, as well as in the mammalian phylogeographic network. Combining these relational structures with both viral-specific and mammal-specific features, the GNN generates node representations that encode both the positional context of the node within the observed network of mammal–virus interactions and node-specific biological features, enabling the identification of biologically realistic associations between mammal species and viruses. The propensity score model is built using a hierarchical Bayesian framework that incorporates mammalian taxonomy, proxies for study effort, and hosts geographic range data. By integrating these complementary factors, the model provides informed propensity score estimates for all mammal-virus associations, accounting for human-centric, geographic, biological, and self-reinforcing biases. Finally, the classifier and propensity score model are jointly used to estimate the probability that a missing association reflects a true but undocumented link, forming the basis for reconstructing the predicted mammal-virus association network.