Inductive link prediction facilitates the discovery of missing links and enables cross-community inference in ecological networks.

Predicting species interactions (links) within ecological networks is crucial for advancing our understanding of ecosystem functioning and responses of communities to environmental changes. Transductive link prediction models are often used but are constrained by sparse, incomplete data and are limited to single networks. We addressed these issues using an inductive link prediction (ILP) approach to predict interactions within and between ecological networks by pooling data across communities and applying transfer learning. We evaluated the performance of our ILP model on 538 networks across four community types: plant-seed disperser, plant-pollinator, host-parasite and plant-herbivore, and found that it achieved higher precision and F scores than transductive models. However, cross-community prediction efficacy varied, with better performance when plant-seed disperser and host-parasite networks were used as training and test sets, compared with when plant-pollinator and plant-herbivore networks were used. Finally, leveraging the generalizability of ILP, we developed a pretrained model that ecologists could readily use to make instant predictions for their networks. This Article highlights the potential of ILP to improve prediction of ecological interactions, enabling generalization across diverse ecological contexts and bridging critical data gaps.