# LabelRank: A Stabilized Label Propagation Algorithm for Community Detection in Networks

arXiv: https://arxiv.org/abs/1303.0868 · HTML: https://mlsanity.com/paper/1303.0868/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2013.

## Built on by (most cited first, 3 of 3)

- [Enhancing the Resilience of Graph Neural Networks to Topological Perturbations in Sparse Graphs](https://mlsanity.com/paper/2406.03097/) (2024)
- [Regularized Optimal Transport for Dynamic Semi-supervised Learning](https://mlsanity.com/paper/2103.11937/) (2021)
- [Information Geometry of Absorbing Markov-Chain and Discriminative Random Walks](https://mlsanity.com/paper/2602.08185/) (2026)

## Abstract (arXiv)

An important challenge in big data analysis nowadays is detection of cohesive groups in large-scale networks, including social networks, genetic networks, communication networks and so. In this paper, we propose LabelRank, an efficient algorithm detecting communities through label propagation. A set of operators is introduced to control and stabilize the propagation dynamics. These operations resolve the randomness issue in traditional label propagation algorithms (LPA), stabilizing the discovered communities in all runs of the same network. Tests on real-world networks demonstrate that LabelRank significantly improves the quality of detected communities compared to LPA, as well as other popular algorithms.