A Survey of Unifying Large Language Models with Graph Learning: Concepts, Methods, and Recent Advancements for Text-attributed Graph

[TechRxiv] Lihui Liu released a review on March 5, 2026, surveying the concepts, methods, and recent progress in integrating large language models with graph learning, with a focus on text-attributed graphs. The paper states that before the rise of LLMs, graph neural networks were the mainstream approach in graph learning, with node embedding, graph convolution, graph attention, and random walk-based techniques widely adopted; when handling graphs with text node attributes, shallow text embeddings are typically used as initial node representations, but the general knowledge captured by such embeddings is limited. The review examines two main routes around tasks such as node classification, link prediction, and graph-based question answering: one is "LLM as enhancer," in which the LLM enriches node text attributes with its knowledge and graph learning methods then make predictions; the other is "LLM as predictor," in which graph data is fed as input to the LLM, which directly solves graph-related tasks. The author states that through a systematic examination of these two directions, observations and new insights on the integration of LLMs and graph learning are provided.