Pith. sign in

REVIEW 4 cited by

Curvature-based Clustering on Graphs

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2307.10155 v1 pith:A32KRSWU submitted 2023-07-19 cs.SI cs.DMcs.LGmath.COstat.ML

Yu Tian , Zachary Lubberts , Melanie Weber This is my paper · ORCID
classification cs.SIcs.DMcs.LGmath.COstat.ML
keywords communitygraphdetectionalgorithmsclusteringcurvature-basedcommunitiescurvature
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Unsupervised node clustering (or community detection) is a classical graph learning task. In this paper, we study algorithms, which exploit the geometry of the graph to identify densely connected substructures, which form clusters or communities. Our method implements discrete Ricci curvatures and their associated geometric flows, under which the edge weights of the graph evolve to reveal its community structure. We consider several discrete curvature notions and analyze the utility of the resulting algorithms. In contrast to prior literature, we study not only single-membership community detection, where each node belongs to exactly one community, but also mixed-membership community detection, where communities may overlap. For the latter, we argue that it is beneficial to perform community detection on the line graph, i.e., the graph's dual. We provide both theoretical and empirical evidence for the utility of our curvature-based clustering algorithms. In addition, we give several results on the relationship between the curvature of a graph and that of its dual, which enable the efficient implementation of our proposed mixed-membership community detection approach and which may be of independent interest for curvature-based network analysis.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Spectro-Riemannian Graph Neural Networks

    cs.LG 2025-02 conditional novelty 6.0 of 10

    CUSP is a graph neural network that combines Ollivier-Ricci curvature with spectral filters on a product of hyperbolic, spherical, and Euclidean spaces, claiming SOTA results on eight node and link prediction benchmarks.

  2. CurvGAD: Leveraging Curvature for Enhanced Graph Anomaly Detection

    cs.LG 2025-02 conditional novelty 5.0 of 10

    CurvGAD adds Ollivier-Ricci curvature reconstruction and Ricci-flow regularization to a graph autoencoder, improving node-level anomaly detection AUROC by up to 6.5% over state-of-the-art baselines on 10 datasets.

  3. Geometric Machine Learning on EEG Signals

    cs.LG 2025-02 reject novelty 5.0 of 10

    An EEG pipeline combining transformer-based denoising with graph Ricci flow and a GCN reports 0.97 accuracy for digit versus non-digit thought classification, but without baselines or code.

  4. Removing Neural Signal Artifacts with Autoencoder-Targeted Adversarial Transformers (AT-AT)

    cs.LG 2025-02 conditional novelty 5.0 of 10

    An autoencoder-gated adversarial transformer denoises EEG-EMG mixtures with reconstruction accuracy comparable to larger published models at a fraction of the model size.

Pith tools