mlsanityData to 14 Oct 2026

GraphChain: Large Language Models for Large-scale Graph Analysis via Tool Chaining

arXiv 2511.00457 ↗First posted 1 Nov 2025cs.AIcited by 5

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Abstract (arXiv)

Large Language Models (LLMs) face significant limitations when applied to large-scale graphs, struggling with context constraints and inflexible reasoning. We present GraphChain, a framework that enables LLMs to analyze complex graphs through dynamic sequences of specialized tools, mimicking human exploratory intelligence. Our approach introduces two key innovations: (1) Progressive Graph Distillation, a reinforcement learning mechanism that generates optimized tool sequences balancing task relevance with information compression, and (2) Structure-aware Test-Time Adaptation, which efficiently tailors tool selection strategies to diverse graph topologies using spectral properties and lightweight adapters without costly retraining. Experiments show GraphChain significantly outperforms prior methods, enabling scalable and adaptive LLM-driven graph analysis.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 62 of its 92 references that are in the map. First posted on arXiv 1 Nov 2025.

Builds on

62 of 92 references.

References · 62

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38 more
  1. 2022Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Modelscited by 382
  2. 2023A Comprehensive Survey on Test-Time Adaptation under Distribution Shiftscited by 359
  3. 2023Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoningcited by 328
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  5. 2023GPT4Tools: Teaching Large Language Model to Use Tools via Self-instructioncited by 248
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50 most cited of 62 All 62 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026GABench: A Comprehensive Benchmark for Evaluating LLM Agents on Graph Analysis Taskscited by 2
  2. 2026GraphSeek: Next-Generation Graph Analytics with LLMscited by 1
  3. 2026Clustering as Reasoning: A k-Means Interpretation of Chain-of-Thought Graph Learningcited by 1
  4. 2026LoReC: Rethinking Large Language Models for Graph Data Analysiscited by 0
  5. 2026Are Large Language Models Suitable for Graph Computation? Progress and Prospectscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability
  2. 2026Teaching LLMs to See Graphs: Unifying Text and Structural Reasoning
  3. 2025Exploring Graph Learning Tasks with Pure LLMs: A Comprehensive Benchmark and Investigation
  4. 2024Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs
  5. 2024GraphArena: Evaluating and Exploring Large Language Models on Graph Computation
  6. 2025GRIP: In-Parameter Graph Reasoning through Fine-Tuning Large Language Models
  7. 2024GUNDAM: Aligning Large Language Models with Graph Understanding
  8. 2025GraphOmni: A Comprehensive and Extensible Benchmark Framework for Large Language Models on Graph-theoretic Tasks