Bridging Semantics and Structure: A Theoretically Grounded Framework for Link Prediction on TAGs
arXiv:2610.40192v1 Announce Type: new Abstract: Link prediction on text-attributed graphs (TAGs) is a fundamental problem with applications in knowledge discovery, recommendation and beyond. Recent progress has highlighted the promise of combining Graph Neural Networks (GNNs) with Language Models (LMs) in order to leverage the complementary structural and semantic signals. However, due to the distinct inductive biases of GNNs and LMs, the design of an optimal fusion strategy remains an open res…