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Identifying the latent space geometry of network models through analysis of curvature
- Source :
- Journal of the Royal Statistical Society Series B: Statistical Methodology. 85:240-292
- Publication Year :
- 2023
- Publisher :
- Oxford University Press (OUP), 2023.
-
Abstract
- A common approach to modelling networks assigns each node to a position on a low-dimensional manifold where distance is inversely proportional to connection likelihood. More positive manifold curvature encourages more and tighter communities; negative curvature induces repulsion. We consistently estimate manifold type, dimension, and curvature from simply connected, complete Riemannian manifolds of constant curvature. We represent the graph as a noisy distance matrix based on the ties between cliques, then develop hypothesis tests to determine whether the observed distances could plausibly be embedded isometrically in each of the candidate geometries. We apply our approach to datasets from economics and neuroscience.
- Subjects :
- Social and Information Networks (cs.SI)
FOS: Computer and information sciences
Statistics and Probability
Geometric Topology (math.GT)
Machine Learning (stat.ML)
Computer Science - Social and Information Networks
Statistics - Applications
Methodology (stat.ME)
Mathematics - Geometric Topology
Statistics - Machine Learning
FOS: Mathematics
Applications (stat.AP)
Statistics, Probability and Uncertainty
Statistics - Methodology
Subjects
Details
- ISSN :
- 14679868 and 13697412
- Volume :
- 85
- Database :
- OpenAIRE
- Journal :
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Accession number :
- edsair.doi.dedup.....6bde91e49eb702175afa6590a6f6d340