Semi-Supervised Text-Attributed Graph Distillation

This research tackles the scalability issue of representing text-rich graphs, aiming to make these complex datasets more manageable for both graph neural networks and large language models. By introducing a novel distillation framework using Wasserstein Distance, it bridges the gap between graph structure and textual semantics, enabling more efficient and effective downstream analysis.