Restless Temporal Path Parameterized Above Lower Bounds
March 29, 2022 Β· Declared Dead Β· π Symposium on Theoretical Aspects of Computer Science
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Philipp Zschoche
arXiv ID
2203.15862
Category
cs.DS: Data Structures & Algorithms
Cross-listed
cs.DM
Citations
6
Venue
Symposium on Theoretical Aspects of Computer Science
Last Checked
4 months ago
Abstract
Reachability questions are one of the most fundamental algorithmic primitives in temporal graphs -- graphs whose edge set changes over discrete time steps. A core problem here is the NP-hard Short Restless Temporal Path: given a temporal graph $\mathcal G$, two distinct vertices $s$ and $z$, and two numbers $Ξ΄$ and $k$, is there a $Ξ΄$-restless temporal $s$-$z$ path of length at most $k$? A temporal path is a path whose edges appear in chronological order and a temporal path is $Ξ΄$-restless if two consecutive path edges appear at most $Ξ΄$ time steps apart from each other. Among others, this problem has applications in neuroscience and epidemiology. While Short Restless Temporal Path is known to be computationally hard, e.g., it is NP-hard for only three time steps and W[1]-hard when parameterized by the feedback vertex number of the underlying graph, it is fixed-parameter tractable when parameterized by the path length $k$. We improve on this by showing that Short Restless Temporal Path can be solved in (randomized) $4^{k-d}|\mathcal G|^{O(1)}$ time, where $d$ is the minimum length of a temporal $s$-$z$ path.
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