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

A
About on SuperTechFans
量子位
Apple Machine Learning Research
Apple Machine Learning Research
博客园 - 聂微东
V
Visual Studio Blog
小众软件
小众软件
Hugging Face - Blog
Hugging Face - Blog
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
博客园 - 司徒正美
V
V2EX
The GitHub Blog
The GitHub Blog
博客园_首页
月光博客
月光博客
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
MyScale Blog
MyScale Blog
博客园 - 叶小钗
F
Fortinet All Blogs
T
Tailwind CSS Blog
GbyAI
GbyAI
酷 壳 – CoolShell
酷 壳 – CoolShell
IT之家
IT之家
WordPress大学
WordPress大学
B
Blog
H
Help Net Security

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
On Computing Vertex Connectivity of 1-Plane Graphs
Therese Biedl, Karthik Murali · 2022-12-14 · via cs.DS updates on arXiv.org

The vertex connectivity of a graph $G$ is the size of the smallest set of vertices $S$ such that $G \setminus S$ is disconnected. For the class of planar graphs, the problem of vertex connectivity is well-studied, both from structural and algorithmic perspectives. Let $G$ be a plane embedded graph, and $Λ(G)$ be an auxiliary graph obtained by inserting a face vertex inside each face and connecting it to all vertices of $G$ incident with the face. If $S$ is a minimal vertex cut of $G$, then there exists a cycle of length $2|S|$ whose vertices alternate between vertices of $S$ and face vertices. This structure facilitates the designing of a linear-time algorithm to find minimum vertex cuts of planar graphs. In this paper, we attempt a similar approach for the class of 1-plane graphs -- these are graphs with a drawing on the plane where each edge is crossed at most once. We consider different classes of 1-plane graphs based on the subgraphs induced by the endpoints of crossings. For 1-plane graphs where the endpoints of every crossing induce the complete graph $K_4$, we show that the structure of minimum vertex cuts is identical to that in plane graphs, as mentioned above. For 1-plane graphs where the endpoints of every crossing induce at least three edges (i.e., one edge apart from the crossing pair of edges), we show that for any minimal vertex cut $S$, there exists a cycle of diameter $O(|S|)$ in $Λ(G)$ such that all vertices of $S$ are in the neighbourhood of the cycle. This structure enables us to design a linear time algorithm to compute the vertex connectivity of all such 1-plane graphs.