# A smart local moving algorithm for large-scale modularity-based community detection

arXiv: https://arxiv.org/abs/1308.6604 · HTML: https://mlsanity.com/paper/1308.6604/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2013.

## Built on by (most cited first, 9 of 9)

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- [A Comprehensive Review of Community Detection in Graphs](https://mlsanity.com/paper/2309.11798/) (2023)
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- [Mapping the Structure and Evolution of Software Testing Research Over the Past Three Decades](https://mlsanity.com/paper/2109.04086/) (2021)
- [GRAPHYP: A Scientific Knowledge Graph with Manifold Subnetworks of Communities. Detection of Scholarly Disputes in Adversarial Information Routes](https://mlsanity.com/paper/2205.01331/) (2022)
- [Dynamic Vertex Replacement Grammars](https://mlsanity.com/paper/2303.11553/) (2023)
- [PASCO (PArallel Structured COarsening): an overlay to speed up graph clustering algorithms](https://mlsanity.com/paper/2412.13592/) (2024)
- [STAGED: A Multi-Agent Neural Network for Learning Cellular Interaction Dynamics](https://mlsanity.com/paper/2507.11660/) (2025)

## Abstract (arXiv)

We introduce a new algorithm for modularity-based community detection in large networks. The algorithm, which we refer to as a smart local moving algorithm, takes advantage of a well-known local moving heuristic that is also used by other algorithms. Compared with these other algorithms, our proposed algorithm uses the local moving heuristic in a more sophisticated way. Based on an analysis of a diverse set of networks, we show that our smart local moving algorithm identifies community structures with higher modularity values than other algorithms for large-scale modularity optimization, among which the popular 'Louvain algorithm' introduced by Blondel et al. (2008). The computational efficiency of our algorithm makes it possible to perform community detection in networks with tens of millions of nodes and hundreds of millions of edges. Our smart local moving algorithm also performs well in small and medium-sized networks. In short computing times, it identifies community structures with modularity values equally high as, or almost as high as, the highest values reported in the literature, and sometimes even higher than the highest values found in the literature.