# Clustering attributed graphs: models, measures and methods

arXiv: https://arxiv.org/abs/1501.01676 · HTML: https://mlsanity.com/paper/1501.01676/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2015.

## Built on by (most cited first, 20 of 20)

- [Correlated Stochastic Block Models: Exact Graph Matching with Applications to Recovering Communities](https://mlsanity.com/paper/2107.06767/) (2021)
- [The Z-Gromov-Wasserstein Distance](https://mlsanity.com/paper/2408.08233/) (2024)
- [Effective and Scalable Clustering on Massive Attributed Graphs](https://mlsanity.com/paper/2102.03826/) (2021)
- [Graph Computing for Financial Crime and Fraud Detection: Trends, Challenges and Outlook](https://mlsanity.com/paper/2103.03227/) (2021)
- [Exact Community Recovery in Correlated Stochastic Block Models](https://mlsanity.com/paper/2203.15736/) (2022)
- [Effective Clustering on Large Attributed Bipartite Graphs](https://mlsanity.com/paper/2405.11922/) (2024)
- [Spectral Subspace Clustering for Attributed Graphs](https://mlsanity.com/paper/2411.11074/) (2024)
- [Leveraging Large Language Models for Effective Label-free Node Classification in Text-Attributed Graphs](https://mlsanity.com/paper/2412.11983/) (2024)
- [Financial Crime & Fraud Detection Using Graph Computing: Application Considerations & Outlook](https://mlsanity.com/paper/2103.01854/) (2021)
- [Adaptive Local Clustering over Attributed Graphs](https://mlsanity.com/paper/2503.20488/) (2025)
- [Effective Clustering for Large Multi-Relational Graphs](https://mlsanity.com/paper/2508.17388/) (2025)
- [A Generative Node-attribute Network Model for Detecting Generalized Structure](https://mlsanity.com/paper/2106.02878/) (2021)
- [Proportional Selection in Networks](https://mlsanity.com/paper/2502.03545/) (2025)
- [Extending Bootstrap AMG for Clustering of Attributed Graphs](https://mlsanity.com/paper/2109.09367/) (2021)
- [Measuring Proximity in Attributed Networks for Community Detection](https://mlsanity.com/paper/2111.03089/) (2021)
- [Goodness-of-Fit of Attributed Probabilistic Graph Generative Models](https://mlsanity.com/paper/2308.03773/) (2023)
- [A stochastic block model for community detection in attributed networks](https://mlsanity.com/paper/2308.16382/) (2023)
- [Unsupervised Learning via Network-Aware Embeddings](https://mlsanity.com/paper/2309.10408/) (2023)
- [Query-Limited Community Recovery in Stochastic Block Models](https://mlsanity.com/paper/2606.02055/) (2026)
- [Towards Adaptive Federated Graph Clustering: A Global Community-aware Contrastive Learning-based Approach](https://mlsanity.com/paper/2609.26063/) (2026)

## Abstract (arXiv)

Clustering a graph, i.e., assigning its nodes to groups, is an important operation whose best known application is the discovery of communities in social networks. Graph clustering and community detection have traditionally focused on graphs without attributes, with the notable exception of edge weights. However, these models only provide a partial representation of real social systems, that are thus often described using node attributes, representing features of the actors, and edge attributes, representing different kinds of relationships among them. We refer to these models as attributed graphs. Consequently, existing graph clustering methods have been recently extended to deal with node and edge attributes. This article is a literature survey on this topic, organizing and presenting recent research results in a uniform way, characterizing the main existing clustering methods and highlighting their conceptual differences. We also cover the important topic of clustering evaluation and identify current open problems.