Graph Machine Learning in the Era of LLMs
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This survey maps the intersection of Graph ML and LLMs, covering both how LLMs enhance graph learning and how graphs (especially knowledge graphs) strengthen LLMs. The authors organize the literature into a taxonomy and highlight where open problems remain.
Dual-direction coverage: Two complementary threads are surveyed - LLMs augmenting GNNs (feature quality, OOD generalization, few-shot learning) and graphs augmenting LLMs (knowledge grounding in pre-training and inference).
Taxonomy of methods: Existing work is categorized by how LLMs interact with graphs - as feature extractors, as predictors, or as integral components of graph pipelines.
Core problem domains: The paper explicitly covers graph heterogeneity, out-of-distribution generalization, explainability, and hallucination mitigation as key challenges at the intersection.
Open directions: Identifies gaps in practical applications, reliable factual grounding from knowledge graphs, and broader empirical evaluation of graph-language approaches.
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