Node Role-Guided LLMs for Dynamic Graph Clustering

TL;DR

This paper introduces DyG-RoLLM, an interpretable framework for dynamic graph clustering that uses role prototypes and hierarchical LLM reasoning to produce explainable community detection results.

Contribution

It proposes a novel end-to-end interpretable method that maps graph embeddings to semantic node roles and integrates LLMs for explainable clustering in dynamic graphs.

Findings

Effective on synthetic and real-world datasets

Provides natural language explanations for clustering decisions

Enhances interpretability and robustness of dynamic graph clustering

Abstract

Dynamic graph clustering aims to detect and track time-varying clusters in dynamic graphs, revealing how complex real-world systems evolve over time. However, existing methods are predominantly black-box models. They lack interpretability in their clustering decisions and fail to provide semantic explanations of why clusters form or how they evolve, severely limiting their use in safety-critical domains such as healthcare or transportation. To address these limitations, we propose an end-to-end interpretable framework that maps continuous graph embeddings into discrete semantic concepts through learnable prototypes. Specifically, we first decompose node representations into orthogonal role and clustering subspaces, so that nodes with similar roles (e.g., hubs, bridges) but different cluster affiliations can be properly distinguished. We then introduce five node role prototypes (Leader, Contributor, Wanderer, Connector, Newcomer) in the role subspace as semantic anchors, transforming continuous embeddings into discrete concepts to facilitate LLM understanding of node roles within communities. Finally, we design a hierarchical LLM reasoning mechanism to generate both clustering results and natural language explanations, while providing consistency feedback as weak supervision to refine node representations. Experimental results on four synthetic and six real-world benchmarks demonstrate the effectiveness, interpretability, and robustness of DyG-RoLLM. Code is available at https://github.com/Clearloveyuan/DyG-RoLLM.