Interpretable Unsupervised Community Detection with LLM-Symbolized Structured Processes

• URL: http://arxiv.org/abs/2608.06402v1
• Date: Sun, 02 Aug 2026 03:30:00 GMT
• Title: Interpretable Unsupervised Community Detection with LLM-Symbolized Structured Processes
• Abstract summary: LUCID is a large language model-guided, interpretable, training-free, and unsupervised community detection method.<n>LLMs induce formal rules that translate implicit knowledge into explicit and interpretable logical structures.<n>Experiments on real-world datasets demonstrate that LUCID, as an unsupervised approach, achieves state-of-the-art performance.
• Score: 17.316321959109526
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Community detection is a fundamental task in graph analytics that aims to identify cohesive groups of entities with similar behaviors or interests. Classic objective-driven methods struggle with complex graph structures, while deep-learning approaches improve performance at the expense of interpretability and rely on labeled data and training. Large language models (LLMs), with strong reasoning capabilities and world knowledge, are promising for interpretable, label-free community detection. To leverage these strengths, we propose LUCID, an LLM-guided, interpretable, training-free, and unsupervised community detection method. Inspired by phase-transition kinetics in natural systems, where complex structures emerge through initialization, merging, refinement, and selection, LUCID is designed as a four-stage pipeline. Within this pipeline, the LLM induces formal rules that translate implicit knowledge into explicit and interpretable logical structures. Specifically, (1) the Local-View Community Initialization stage encodes local graph structures using k-ego contexts and unsupervised node roles; (2) the Multi-factor Community Merge stage uses LLM-induced rules to iteratively merge local communities; (3) the Multi-grain Community Refinement stage applies LLM-induced coarse-to-fine rules in parallel to reduce boundary noise; and (4) the Global-view Community Selection stage identifies high-quality communities based on topological compactness and boundary clarity. Extensive experiments on real-world datasets demonstrate that LUCID, as an unsupervised approach, achieves state-of-the-art performance and consistently outperforms leading unsupervised and semi-supervised baselines.

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