Lower Ricci Curvature for Efficient Community Detection

Abstract

This study introduces the Lower Ricci Curvature (LRC), a novel, scalable, and scale-free discrete curvature designed to enhance community detection in networks. Addressing the computational challenges posed by existing curvature-based methods, LRC offers a streamlined approach with linear computational complexity, which makes it well suited for large-scale network analysis. We further develop an LRC-based preprocessing method that effectively augments popular community detection algorithms. Through applications on multiple real-world datasets, including the NCAA football league network, the DBLP collaboration network, the Amazon product co-purchasing network, and the YouTube social network, we demonstrate the efficacy of our method in significantly improving the performance of various community detection algorithms.

Cite

Text

Park and Li. "Lower Ricci Curvature for Efficient Community Detection." Transactions on Machine Learning Research, 2025.

Markdown

[Park and Li. "Lower Ricci Curvature for Efficient Community Detection." Transactions on Machine Learning Research, 2025.](https://mlanthology.org/tmlr/2025/park2025tmlr-lower/)

BibTeX

@article{park2025tmlr-lower,
  title     = {{Lower Ricci Curvature for Efficient Community Detection}},
  author    = {Park, Yun Jin and Li, Didong},
  journal   = {Transactions on Machine Learning Research},
  year      = {2025},
  url       = {https://mlanthology.org/tmlr/2025/park2025tmlr-lower/}
}