# LLMs Between the Nodes: Community Discovery Beyond Vectors

arXiv: https://arxiv.org/abs/2507.22955 · HTML: https://mlsanity.com/paper/2507.22955/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 33 references that are in the map.
- First posted on arXiv 29 Jul 2025.

## Builds on (most cited first, 19 of 19)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [A Survey on LLM-as-a-Judge](https://mlsanity.com/paper/2411.15594/) (2024)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Can Language Models Solve Graph Problems in Natural Language?](https://mlsanity.com/paper/2305.10037/) (2023)
- [GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking](https://mlsanity.com/paper/2305.15066/) (2023)
- [Talk like a Graph: Encoding Graphs for Large Language Models](https://mlsanity.com/paper/2310.04560/) (2023)
- [PlanGenLLMs: A Modern Survey of LLM Planning Capabilities](https://mlsanity.com/paper/2502.11221/) (2025)
- [A Complete Survey on LLM-based AI Chatbots](https://mlsanity.com/paper/2406.16937/) (2024)
- [Hierarchical Block Structures and High-resolution Model Selection in Large Networks](https://mlsanity.com/paper/1310.4377/) (2013)
- [GraphTool-Instruction: Revolutionizing Graph Reasoning in LLMs through Decomposed Subtask Instruction](https://mlsanity.com/paper/2412.12152/) (2024)
- [t-PINE: Tensor-based Predictable and Interpretable Node Embeddings](https://mlsanity.com/paper/1805.01889/) (2018)
- [ComGPT: Detecting Local Community Structure with Large Language Models](https://mlsanity.com/paper/2408.06658/) (2024)
- [Identification of Overlapping Communities via Constrained Egonet Tensor Decomposition](https://mlsanity.com/paper/1707.04607/) (2017)
- [MNTD: An Efficient Dynamic Community Detector Based on Nonnegative Tensor Decomposition](https://mlsanity.com/paper/2407.18849/) (2024)

## Built on by (most cited first, 1 of 1)

- [Node Role-Guided LLMs for Dynamic Graph Clustering](https://mlsanity.com/paper/2603.13799/) (2026)

## Abstract (arXiv)

Community detection in social network graphs plays a vital role in uncovering group dynamics, influence pathways, and the spread of information. Traditional methods focus primarily on graph structural properties, but recent advancements in Large Language Models (LLMs) open up new avenues for integrating semantic and contextual information into this task. In this paper, we present a detailed investigation into how various LLM-based approaches perform in identifying communities within social graphs. We introduce a two-step framework called CommLLM, which leverages the GPT-4o model along with prompt-based reasoning to fuse language model outputs with graph structure. Evaluations are conducted on six real-world social network datasets, measuring performance using key metrics such as Normalized Mutual Information (NMI), Adjusted Rand Index (ARI), Variation of Information (VOI), and cluster purity. Our findings reveal that LLMs, particularly when guided by graph-aware strategies, can be successfully applied to community detection tasks in small to medium-sized graphs. We observe that the integration of instruction-tuned models and carefully engineered prompts significantly improves the accuracy and coherence of detected communities. These insights not only highlight the potential of LLMs in graph-based research but also underscore the importance of tailoring model interactions to the specific structure of graph data.