惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

aimingoo的专栏
aimingoo的专栏
Jina AI
Jina AI
WordPress大学
WordPress大学
Recent Announcements
Recent Announcements
G
Google Developers Blog
I
InfoQ
H
Hackread – Cybersecurity News, Data Breaches, AI and More
Google DeepMind News
Google DeepMind News
P
Proofpoint News Feed
MyScale Blog
MyScale Blog
M
MIT News - Artificial intelligence
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
C
Check Point Blog
J
Java Code Geeks
T
Tailwind CSS Blog
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
Microsoft Security Blog
Microsoft Security Blog
MongoDB | Blog
MongoDB | Blog
V
Visual Studio Blog
人人都是产品经理
人人都是产品经理
量子位
A
About on SuperTechFans
D
DataBreaches.Net
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知

cs.DS updates on arXiv.org

PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
Bow Metrics and Hyperbolicity
Feodor F. Dragan, Guillaume Ducoffe, Michel Habib, Laurent Vienn · 2024-11-26 · via cs.DS updates on arXiv.org

A ($λ,μ$)-bow metric was defined in (Dragan & Ducoffe, 2023) as a far reaching generalization of an $α_i$-metric (which is equivalent to a ($0,i$)-bow metric). A graph $G=(V,E)$ is said to satisfy ($λ,μ$)-bow metric if for every four vertices $u,v,w,x$ of $G$ the following holds: if two shortest paths $P(u,w)$ and $P(v,x)$ share a common shortest subpath $P(v,w)$ of length more than $λ$ (that is, they overlap by more than $λ$), then the distance between $u$ and $x$ is at least $d_G(u,v)+d_G(v,w)+d_G(w,x)-μ$. ($λ,μ$)-Bow metric can also be considered for all geodesic metric spaces. It was shown by Dragan & Ducoffe that every $δ$-hyperbolic graph (in fact, every $δ$-hyperbolic geodesic metric space) satisfies ($δ, 2δ$)-bow metric. Thus, ($λ,μ$)-bow metric is a common generalization of hyperbolicity and of $α_i$-metric. In this paper, we investigate an intriguing question whether ($λ,μ$)-bow metric implies hyperbolicity in graphs. Note that, this is not the case for general geodesic metric spaces as Euclidean spaces satisfy ($0,0$)-bow metric whereas they have unbounded hyperbolicity. We conjecture that, in graphs, ($λ,μ$)-bow metric indeed implies hyperbolicity and show that our conjecture is true for several large families of graphs.