One Model, Three Levels: Multi-Task Learning on Hypergraphs

Developing a single model for multiple tasks has long been a goal in artificial intelligence. In hypergraph learning, existing work has explored using a single model to handle node-level and hyperedge-level tasks, but has not incorporated hypergraph-level tasks into the same model. Therefore, we consider the problem of handling node-level, hyperedge-level, and hypergraph-level tasks within a single model, which poses two challenges. First, these tasks involve prediction targets at different structural levels and differ in their input and prediction formats. Second, their optimization objectives may interfere with one another, so improving performance on one task may degrade performance on another. To address these challenges, we propose a model for multi-task learning on hypergraphs. To unify the input and prediction formats across the three tasks, the proposed model uses query-marked subhypergraphs as a common input representation and applies a common query readout followed by prototype matching to produce predictions in a common format. To mitigate cross-task interference, it combines a shared hypergraph encoder with task-conditioned adapters, enabling the model to capture shared higher-order structural patterns while preserving task-specific information. Experimental results demonstrate that the proposed model performs comparably to the baselines across node-, hyperedge-, and hypergraph-level tasks while mitigating cross-task interference. The code is available at: https://anonymous.4open.science/r/HyperOTri-7EFB.