Same Graph Cross-Task Transfer in GNNs: Protocols and Predictors
When graph neural networks learn two tasks at once, which direction helps more?
When a neural network learns to predict on the same graph in two different ways—classifying nodes and predicting missing links—one direction of knowledge transfer works reliably while the other often fails. The researchers found that teaching the network link prediction first, then node classification, almost always improves performance on homophilic graphs (where similar nodes connect), but going the opposite direction produces fragile results that can actually hurt accuracy if not done carefully.
Many real-world systems—social networks, recommendation engines, knowledge graphs—need to make multiple kinds of predictions on the same underlying structure. This work provides the first reliable benchmark for when you can reuse what a network learns from one task to improve another, and shows that simple graph properties like homophily can tell you in advance whether sharing will help or hurt. That means engineers can avoid wasting computation on transfer learning strategies that will backfire.