| 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. |
| Outcome: | The proposed model surpasses all GNN baselines on ogbn-arxiv, Cora and PubMed datasets and sheds light on generative LLMs as new foundation model for graph machine learning. |
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| 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. |
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
| Challenge: | Large Language Models (LLMs) have been used for graph discriminative tasks, but their potential for graph structure generation remains unexplored. |
| Approach: | They propose to use LLMs to generate graphs that optimize network properties by injecting domain expertise from network science into the code. |
| Outcome: | The proposed model generates graphs satisfying each property in different domains and compares it with established graph generative models across multiple domains. |
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. |
| Outcome: | The proposed models outperform traditional graph learning models in few-shot settings, the authors show . their models out perform models with instruction tuning, and they show excellent generalization and robustness. |
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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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. |
| Approach: | They propose a Graph Language Model (GLM) that integrates the strengths of both approaches and mitigates their weaknesses. |
| Outcome: | Empirical evaluations show that the proposed model surpasses both LM- and GNN-based baselines in supervised and zero-shot setting, demonstrating their versatility. |
InstructGraph: Boosting Large Language Models via Graph-centric Instruction Tuning and Preference Alignment (2024.findings-acl)
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| Challenge: | Existing large language models (LLMs) can solve graph reasoning and generation tasks with parameter updates without sacrificing performance. |
| Approach: | They propose a structured format verbalizer to unify all graph data into a universal code-like format, which can simply represent the graph without any external graph-specific encoders. |
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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. |
| Approach: | They propose a human-interpretable structural encoding strategy that injects graph structure directly into natural language prompts. |
| Outcome: | The proposed method improves performance on synthetic and real-world datasets. |
Evaluating and Improving Graph to Text Generation with Large Language Models (2025.naacl-long)
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| Challenge: | Recent advances in large language models have revolutionized natural language processing due to their zero-and-short-shot capabilities. |
| Approach: | They propose a tuning-free prompting approach for graph-to-text generation tasks. |
| Outcome: | The proposed approach improves LLMs on graph-to-text generation tasks incrementally. |
Can Large Language Models Tackle Graph Partitioning? (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) have remarkable capabilities in understanding complex tasks, but they can only handle graph partitioning tasks that require global perception abilities. |
| Approach: | They propose a pipeline for coarsening, reasoning, and refining to enable LLMs to perform graph partitioning on small-scale graphs. |
| Outcome: | The proposed pipeline can handle graph partitioning tasks on small graphs with coarsening, reasoning, and refining. |
Large Language Models are Good Relational Learners (2025.acl-long)
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| Challenge: | Existing approaches to serialize large language models disregard critical relational structures and creates redundancies. |
| Approach: | They propose a graph neural network encoder to create structured relational prompts for large language models within a retrieval-augmented generation framework. |
| Outcome: | The proposed architecture preserves relational structure of databases while enabling LLMs to process and reason over complex entity relationships. |