Large network community detection by fast label propagation
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Abstract (arXiv)
Many networks exhibit some community structure. There exists a wide variety of approaches to detect communities in networks, each offering different interpretations and associated algorithms. For large networks, there is the additional requirement of speed. In this context, the so-called label propagation algorithm (LPA) was proposed, which runs in near-linear time. In partitions uncovered by LPA, each node is ensured to have most links to its assigned community. We here propose a fast variant of LPA (FLPA) that is based on processing a queue of nodes whose neighbourhood recently changed. We test FLPA exhaustively on benchmark networks and empirical networks, finding that it can run up to 700 times faster than LPA. In partitions found by FLPA, we prove that each node is again guaranteed to have most links to its assigned community. Our results show that FLPA is generally preferable to LPA.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Sep 2022.
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Citing papers per year: 2023: 1; 2024: 0; 2025: 0; 2026: 4.
5 papers.
- 2023A Comprehensive Review of Community Detection in Graphscited by 12
- 2026Probabilistic Label Spreading: Efficient and Consistent Estimation of Soft Labels with Epistemic Uncertainty on Graphscited by 1
- 2026Efficient Recommendations via Graph Coarsening and Label Propagationcited by 0
- 2026Semantic Networks as Clues: A Theoretical Foundation and Process Optimization for Semantic Network Constructioncited by 0
- 2026ComNetX: Local Hierarchical Adaptation for Dynamic Community Detectioncited by 0
- 2026Semantic Networks as Clues: A Theoretical Foundation and Process Optimization for Semantic Network Construction3 Aug 2026
- 2026Efficient Recommendations via Graph Coarsening and Label Propagation24 Jul 2026
- 2026ComNetX: Local Hierarchical Adaptation for Dynamic Community Detection10 Jul 2026
- 2026Probabilistic Label Spreading: Efficient and Consistent Estimation of Soft Labels with Epistemic Uncertainty on Graphs4 Feb 2026
- 2023A Comprehensive Review of Community Detection in Graphs21 Sep 2023