Same Graph Cross-Task Transfer in GNNs: Protocols and Predictors
arXiv:2607.28525
The paper defines a clean evaluation protocol for transferring knowledge between node classification and link prediction on the same graph, shows that transfer is directionally dependent (NC→LP helps on homophilic graphs while LP→NC is fragile), and introduces a CoTask Score to assess joint task performance.
Abstract
Many real-world graphs support multiple predictive tasks over the same underlying structure, creating an opportunity to reuse supervision across node classification (NC) and link prediction (LP). However, existing evaluations often rely on incompatible splits, observed-graph assumptions, and negative sampling rules, making conclusions about same-graph cross-task transfer unreliable. We formalize same-graph NC-LP transfer and propose a leakage-free protocol that fixes node and edge splits, uses a shared message-passing graph that excludes evaluated edges, and employs fixed negatives for LP. Across three backbones (GCN, GraphSAGE, GPS), we find that transfer is strongly directional and predictable: NC LP is consistently beneficial on homophilic graphs, while LP NC is fragile and can even degrade accuracy under naive representation reuse. LP NC becomes reliably positive mainly in a structure-dominant regime where LP is easy but NC is unsaturated, suggesting that LP acts as structural pretraining. Finally, we introduce the CoTask Score (CTS) to summarize joint NC+LP utility when a shared encoder must serve both tasks, and show that simple dataset statistics, especially homophily, can guide mechanism choice and help avoid negative transfer.
17 pages, 2 figures