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

Recent Announcements
Recent Announcements
J
Java Code Geeks
雷峰网
雷峰网
Microsoft Security Blog
Microsoft Security Blog
博客园 - 【当耐特】
腾讯CDC
博客园 - 司徒正美
B
Blog RSS Feed
博客园 - 三生石上(FineUI控件)
I
InfoQ
N
Netflix TechBlog - Medium
L
LangChain Blog
博客园_首页
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
T
Tailwind CSS Blog
MyScale Blog
MyScale Blog
美团技术团队
The Cloudflare Blog
爱范儿
爱范儿
Stack Overflow Blog
Stack Overflow Blog
博客园 - 聂微东
H
Help Net Security
Martin Fowler
Martin Fowler
V
Visual Studio Blog

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
Distributed Detection of Cliques in Dynamic Networks
Matthias Bonne, Keren Censor-Hillel · 2019-04-26 · via cs.DS updates on arXiv.org

This paper provides an in-depth study of the fundamental problems of finding small subgraphs in distributed dynamic networks. While some problems are trivially easy to handle, such as detecting a triangle that emerges after an edge insertion, we show that, perhaps somewhat surprisingly, other problems exhibit a wide range of complexities in terms of the trade-offs between their round and bandwidth complexities. In the case of triangles, which are only affected by the topology of the immediate neighborhood, some end results are: \begin{itemize} \item The bandwidth complexity of $1$-round dynamic triangle detection or listing is $Θ(1)$. \item The bandwidth complexity of $1$-round dynamic triangle membership listing is $Θ(1)$ for node/edge deletions, $Θ(n^{1/2})$ for edge insertions, and $Θ(n)$ for node insertions. \item The bandwidth complexity of $1$-round dynamic triangle membership detection is $Θ(1)$ for node/edge deletions, $O(\log n)$ for edge insertions, and $Θ(n)$ for node insertions. \end{itemize} Most of our upper and lower bounds are \emph{tight}. Additionally, we provide almost always tight upper and lower bounds for larger cliques.