# Forman-Ricci flow for change detection in large dynamic data sets

arXiv: https://arxiv.org/abs/1604.06634 · HTML: https://mlsanity.com/paper/1604.06634/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2016.

## Built on by (most cited first, 7 of 7)

- [Graph Pooling via Ricci Flow](https://mlsanity.com/paper/2407.04236/) (2024)
- [Continuum Limits of Ollivier's Ricci Curvature on data clouds: pointwise consistency and global lower bounds](https://mlsanity.com/paper/2307.02378/) (2023)
- [Deep Learning as Ricci Flow](https://mlsanity.com/paper/2404.14265/) (2024)
- [Curved Markov Chain Monte Carlo for Network Learning](https://mlsanity.com/paper/2110.03413/) (2021)
- [Geometric Flow enhanced Graph Coarsening](https://mlsanity.com/paper/2609.14962/) (2026)
- [Ironing the Graphs: Toward a Correct Geometric Analysis of Large-Scale Graphs](https://mlsanity.com/paper/2407.21609/) (2024)
- [GFLC: Graph-based Fairness-aware Label Correction for Fair Classification](https://mlsanity.com/paper/2506.15620/) (2025)

## Abstract (arXiv)

We present a viable solution to the challenging question of change detection in complex networks inferred from large dynamic data sets. Building on Forman's discretization of the classical notion of Ricci curvature, we introduce a novel geometric method to characterize different types of real-world networks with an emphasis on peer-to-peer networks. Furthermore we adapt the classical Ricci flow that already proved to be a powerful tool in image processing and graphics, to the case of undirected and weighted networks. The application of the proposed method on peer-to-peer networks yields insights into topological properties and the structure of their underlying data.