  We present Simplified Text-Attributed Graph Embeddings (STAGE), a
straightforward yet effective method for enhancing node features in Graph
Neural Network (GNN) models that encode Text-Attributed Graphs (TAGs). Our
approach leverages Large-Language Models (LLMs) to generate embeddings for
textual attributes. STAGE achieves competitive results on various node
classification benchmarks while also maintaining a simplicity in implementation
relative to current state-of-the-art (SoTA) techniques. We show that utilizing
pre-trained LLMs as embedding generators provides robust features for ensemble
GNN training, enabling pipelines that are simpler than current SoTA approaches
which require multiple expensive training and prompting stages. We also
implement diffusion-pattern GNNs in an effort to make this pipeline scalable to
graphs beyond academic benchmarks.
