Link prediction models can benefit from incorporating textual descriptions of entities and relations, enabling fully inductive learning and flexibility in dynamic graphs. We address the challenge of also capturing rich structured information about the local neighbourhood of entities and their relations, by introducing a Transformer-based approach that effectively integrates textual descriptions with graph structure, reducing the reliance on resource-intensive text encoders. Our experiments on three challenging datasets show that our Fast-and-Frugal Text-Graph (FnF-TG) Transformers achieve superior performance compared to the previous state-of-the-art methods, while maintaining efficiency and scalability.
View on arXiv@article{coman2025_2408.06778,
title={ Fast-and-Frugal Text-Graph Transformers are Effective Link Predictors },
author={ Andrei C. Coman and Christos Theodoropoulos and Marie-Francine Moens and James Henderson },
journal={arXiv preprint arXiv:2408.06778},
year={ 2025 }
}