ArticleScientific reports2019
Community Detection on Networks with Ricci Flow.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 125 citations in OpenAlex.
- Graph Ricci curvatures reveal atypical functional connectivity in autism spectrum disorder.Scientific reports · 2022Pooled it
- Parallel median consensus clustering in complex networks.Scientific reports · 2025Article
- GraphBNC: Machine Learning-Aided Prediction of Interactions Between Metal Nanoclusters and Blood Proteins.Advanced materials (Deerfield Beach, Fla.) · 2024Article
- Deep learning as Ricci flow.Scientific reports · 2024Article
- Charting cellular differentiation trajectories with Ricci flow.Nature communications · 2024Article
- Deeper Exploiting Graph Structure Information by Discrete Ricci Curvature in a Graph Transformer.Entropy (Basel, Switzerland) · 2023Article
- Unfolding the multiscale structure of networks with dynamical Ollivier-Ricci curvature.Nature communications · 2021Article
- Understanding the Variability in Graph Data Sets through Statistical Modeling on the Stiefel Manifold.Entropy (Basel, Switzerland) · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many complex networks in the real world have community structures - groups of well-connected nodes with important functional roles. It has been well recognized that the identification of communities bears numerous practical applications. While existing approaches mainly apply statistical or graph theoretical/combinatorial methods for community detection, in this paper, we present a novel geometric approach which enables us to borrow powerful classical geometric methods and properties. By considering networks as geometric objects and communities in a network as a geometric decomposition, we apply curvature and discrete Ricci flow, which have been used to decompose smooth manifolds with astonishing successes in mathematics, to break down communities in networks. We tested our method on networks with ground-truth community structures, and experimentally confirmed the effectiveness of this geometric approach.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.