paper-with-me

홈 › Papers

Geodesic Length Distribution in Sparse Network Ensembles

2021-11-03 · Sahil Loomba, Nick S. Jones

A key task in the study of networked systems is to derive local and global properties that impact connectivity, synchronizability, and robustness; computing shortest paths or geodesics yields measures of network connectivity that can explain such phenomena. We derive an analytic distribution of geodesic lengths on the giant component in the supercritical regime -- when the giant component exists -- or on small components in the subcritical regime, of any sparse (and possibly directed) network with conditionally independent edges, in the infinite-size limit. We provide specific results for widely used network models like stochastic block models, dot product graphs, random geometric graphs, and sparse graphons. The survival function of the geodesic length distribution possesses a simple closed-form expression which is asymptotically tight for finite lengths, has a natural interpretation of traversing independent geodesics in the network, and delivers novel insight into the aforementioned network families.

📄 PDF Abstract BibTeX arXiv:2111.02330

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Fréchet Geodesic Boosting

2025-09-22 · Yidong Zhou, Su I Iao, Hans-Georg Müller arxiv

Gradient boosting has become a cornerstone of machine learning, enabling base learners such as decision trees to achieve exceptional predictive performance. While existing algorithms primarily handle scalar or Euclidean …

Approximation and bounding techniques for the Fisher-Rao distances between parametric statistical models

2024-03-15 · Frank Nielsen

The Fisher-Rao distance between two probability distributions of a statistical model is defined as the Riemannian geodesic distance induced by the Fisher information metric. In order to calculate the Fisher-Rao distance …

Geodesic Models with Convexity Shape Prior

2021-11-01 · Da Chen, Jean-Marie Mirebeau, Minglei Shu, Xuecheng Tai 외

The minimal geodesic models based on the Eikonal equations are capable of finding suitable solutions in various image segmentation scenarios. Existing geodesic-based segmentation approaches usually exploit image features…

Image SegmentationSegmentationSemantic Segmentation

An Elastica Geodesic Approach With Convexity Shape Prior

2021-01-01 · ICCV 2021 10 · Da Chen, Laurent D. Cohen, Jean-Marie Mirebeau, Xue-Cheng Tai

The minimal geodesic models based on the Eikonal equations are capable of finding suitable solutions in various image segmentation scenarios. Existing geodesic-based segmentation approaches usually exploit the image …

Image SegmentationSegmentationSemantic Segmentation

Manifold-augmented Eikonal Equations: Geodesic Distances and Flows on Differentiable Manifolds

2023-10-09 · Daniel Kelshaw, Luca Magri

Manifolds discovered by machine learning models provide a compact representation of the underlying data. Geodesics on these manifolds define locally length-minimising curves and provide a notion of distance, which are ke…