Near-Optimal Distance Emulator for Planar Graphs
July 04, 2018 Β· Declared Dead Β· π Embedded Systems and Applications
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Hsien-Chih Chang, PaweΕ Gawrychowski, Shay Mozes, Oren Weimann
arXiv ID
1807.01478
Category
cs.DS: Data Structures & Algorithms
Citations
15
Venue
Embedded Systems and Applications
Last Checked
3 months ago
Abstract
Given a graph $G$ and a set of terminals $T$, a \emph{distance emulator} of $G$ is another graph $H$ (not necessarily a subgraph of $G$) containing $T$, such that all the pairwise distances in $G$ between vertices of $T$ are preserved in $H$. An important open question is to find the smallest possible distance emulator. We prove that, given any subset of $k$ terminals in an $n$-vertex undirected unweighted planar graph, we can construct in $\tilde O(n)$ time a distance emulator of size $\tilde O(\min(k^2,\sqrt{k\cdot n}))$. This is optimal up to logarithmic factors. The existence of such distance emulator provides a straightforward framework to solve distance-related problems on planar graphs: Replace the input graph with the distance emulator, and apply whatever algorithm available to the resulting emulator. In particular, our result implies that, on any unweighted undirected planar graph, one can compute all-pairs shortest path distances among $k$ terminals in $\tilde O(n)$ time when $k=O(n^{1/3})$.
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