A simple upper bound for trace function of a hypergraph with applications
February 22, 2019 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Farhad Shahrokhi
arXiv ID
1902.08366
Category
cs.DS: Data Structures & Algorithms
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
Let ${H}=(V, {E})$ be a hypergraph on the vertex set $V$ and edge set ${E}\subseteq 2^V$. We show that number of distinct {\it traces} on any $k-$ subset of $V$, is most $k.{\hat Ξ±}(H)$, where ${\hat Ξ±}(H)$ is the {\it degeneracy} of $H$. The result significantly improves/generalizes some of related results. For instance, the $vc$ dimension $H$ (or $vc(H)$) is shown to be at most $\log({\hat Ξ±}(H))+1$ which was not known before. As a consequence $vc(H)$ can be computed in computed in $n^{O( {\rm log}({\hat Ξ΄}(H)))}$ time. When applied to the neighborhood systems of a graphs excluding a fixed minor, it reduces the known linear upper bound on the $VC$ dimension to a logarithmic one, in the size of the minor. When applied to the location domination and identifying code numbers of any $n$ vertex graph $G$, one gets the new lower bound of $Ξ©(n/({\hat Ξ±}(G))$, where ${\hat Ξ±}(G)$ is the degeneracy of $G$.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Data Structures & Algorithms
π
π
The Cartographer
R.I.P.
π»
Ghosted
Route Planning in Transportation Networks
R.I.P.
π»
Ghosted
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
R.I.P.
π»
Ghosted
Hierarchical Clustering: Objective Functions and Algorithms
R.I.P.
π»
Ghosted
Graph Isomorphism in Quasipolynomial Time
π
π
The Cartographer
Simulation optimization: A review of algorithms and applications
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted