In this paper, we prove an Azuma-Hoeffding-type inequality in several classical models of random configurations by a Ricci curvature approach. Adapting Ollivier’s work on the Ricci curvature of Markov chains on metric spaces, we prove a cleaner form of the corresponding concentration inequality in graphs. Namely, we show that for any Lipschitz function f on any graph (equipped with an ergodic random walk and thus an invariant distribution ν) with Ricci curvature at least κ > 0, we have ν (|f − Eνf| ≥ t) ≤ 2 exp(−t^2κ/7).
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Ollivier Curvature of Random Geometric Graphs Converges to Ricci Curvature of Their Riemannian Manifolds
Abstract Curvature is a fundamental geometric characteristic of smooth spaces. In recent years different notions of curvature have been developed for combinatorial discrete objects such as graphs. However, the connections between such discrete notions of curvature and their smooth counterparts remain lurking and moot. In particular, it is not rigorously known if any notion of graph curvature converges to any traditional notion of curvature of smooth space. Here we prove that in proper settings the Ollivier–Ricci curvature of random geometric graphs in Riemannian manifolds converges to the Ricci curvature of the manifold. This is the first rigorous result linking curvature of random graphs to curvature of smooth spaces. Our results hold for different notions of graph distances, including the rescaled shortest path distance, and for different graph densities. Here the scaling of the average degree, as a function of the graph size, can range from nearly logarithmic to nearly linear.
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- Award ID(s):
- 1741355
- PAR ID:
- 10463782
- Publisher / Repository:
- Springer Nature
- Date Published:
- Journal Name:
- Discrete & Computational Geometry
- Volume:
- 70
- Issue:
- 3
- ISSN:
- 0179-5376
- Page Range / eLocation ID:
- 671
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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