| Challenge: | Natural language is used to describe graphs, but graph descriptions become verbose and only relying on attribute embeddings limits LLM’s ability to capture adequate graph structural information. |
| Approach: | They propose a graph-defined language for large language model that translates the graph into a corpus instead of graph descriptions and pre-trains LLMs on this corpus to adequately understand the graph. |
| Outcome: | Experiments on five datasets show that the proposed framework outperforms description-based and embedding-based baselines by efficiently modeling different orders of neighbors. |
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| Challenge: | Existing work on integrating graph problems into generative language modeling framework remains limited. |
| Approach: | They propose an LLM with instructions based on natural language to perform graph tasks. |
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Exploring Graph Learning Tasks with Pure LLMs: A Comprehensive Benchmark and Investigation (2026.findings-acl)
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| Challenge: | Recent studies focus on performance benchmarks without fully comparing LLMs to graph learning models. |
| Approach: | They evaluate off-the-shelf and instruction-tuned graph learning models across a variety of scenarios. |
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Graph Language Models (2024.acl-long)
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| Challenge: | Language Models (LMs) are the workhorses of NLP, but their interplay with structured knowledge graphs (KGs) is still actively researched. |
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Demystifying the Power of Large Language Models in Graph Generation (2025.findings-naacl)
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Yu Wang, Ryan A. Rossi, Namyong Park, Nesreen K. Ahmed, Danai Koutra, Franck Dernoncourt, Tyler Derr
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Graph-Assisted Large Language Models: A Perspective on Mitigating Intrinsic Limitations (2026.findings-acl)
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Haitong Luo, Fali Wang, Weiyao Zhang, Xianren Zhang, Zhiwei Zhang, Tianxiang Zhao, Minhua Lin, Jiahao Zhang, Hui Liu, Xianfeng Tang, Qi He, Suhang Wang, Xuying Meng, Yujun Zhang
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GraphEval36K: Benchmarking Coding and Reasoning Capabilities of Large Language Models on Graph Datasets (2025.findings-naacl)
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| Challenge: | Large language models (LLMs) have demonstrated significant capabilities in processing and understanding text data. |
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Can LLMs Convert Graphs to Text-Attributed Graphs? (2025.naacl-long)
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| Challenge: | Existing approaches to model graph-structured data are limited by the availability of text-attributed graph data. |
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Colorful Talks with Graphs: Human-Interpretable Graph Encodings for Large Language Models (2026.findings-acl)
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| Challenge: | Graph problems require reasoning over explicit structure, permutation invariance, and computationally complex relationships, creating a mismatch with the representations of text-based models. |
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MSG-LLM: A Multi-scale Interactive Framework for Graph-enhanced Large Language Models (2025.coling-main)
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| Challenge: | Existing graph-enhanced large language models (LLMs) focus on matching subgraphs between subgraph and candidate subgraph at the same scale, neglecting that subgraph with different scales may also share similar semantics or structures. |
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Exploring the Potential of Large Language Models for Heterophilic Graphs (2025.naacl-long)
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| Challenge: | Existing approaches for heterophilic graphs overlook rich textual data associated with nodes, which could unlock deeper insights into their heterophilistic contexts. |
| Approach: | They propose a two-stage framework to enhance node classification on heterophilic graphs by leveraging open-world knowledge encoded by large language models. |
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