Dr George Mertzios george.mertzios@durham.ac.uk
Associate Professor
Sliding Window Temporal Graph Coloring
Mertzios, G.B.; Molter, H.; Zamaraev, V.
Authors
H. Molter
V. Zamaraev
Abstract
Graph coloring is one of the most famous computational problems with applications in a wide range of areas such as planning and scheduling, resource allocation, and pattern matching. So far coloring problems are mostly studied on static graphs, which often stand in stark contrast to practice where data is inherently dynamic and subject to discrete changes over time. A temporal graph is a graph whose edges are assigned a set of integer time labels, indicating at which discrete time steps the edge is active. In this paper we present a natural temporal extension of the classical graph coloring problem. Given a temporal graph and a natural number ∆, we ask for a coloring sequence for each vertex such that (i) in every sliding time window of ∆ consecutive time steps, in which an edge is active, this edge is properly colored (i.e. its endpoints are assigned two different colors) at least once during that time window, and (ii) the total number of different colors is minimized. This sliding window temporal coloring problem abstractly captures many realistic graph coloring scenarios in which the underlying network changes over time, such as dynamically assigning communication channels to moving agents. We present a thorough investigation of the computational complexity of this temporal coloring problem. More specifically, we prove strong computational hardness results, complemented by efficient exact and approximation algorithms. Some of our algorithms are linear-time fixed-parameter tractable with respect to appropriate parameters, while others are asymptotically almost optimal under the Exponential Time Hypothesis (ETH).
Citation
Mertzios, G., Molter, H., & Zamaraev, V. (2019). Sliding Window Temporal Graph Coloring. In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (7667-7674). https://doi.org/10.1609/aaai.v33i01.33017667
Conference Name | 33rd AAAI Conference on Artificial Intelligence (AAAI 2019). |
---|---|
Conference Location | Honolulu, Hawaii, USA |
Start Date | Jan 27, 2023 |
End Date | Feb 1, 2019 |
Acceptance Date | Oct 31, 2018 |
Online Publication Date | Jul 17, 2019 |
Publication Date | Jul 17, 2019 |
Deposit Date | Nov 14, 2018 |
Publicly Available Date | Mar 29, 2024 |
Volume | 33 |
Pages | 7667-7674 |
Series Number | 1 |
Series ISSN | 2159-5399,2374-3468 |
Book Title | Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence. |
DOI | https://doi.org/10.1609/aaai.v33i01.33017667 |
Publisher URL | https://doi.org/10.1609/aaai.v33i01.33017667 |
Related Public URLs | https://arxiv.org/abs/1811.04753 |
Files
Accepted Conference Proceeding
(264 Kb)
PDF
You might also like
Graphs with minimum fractional domatic number
(2023)
Journal Article
Approximate and Randomized algorithms for Computing a Second Hamiltonian Cycle
(2023)
Journal Article
Sliding into the Future: Investigating Sliding Windows in Temporal Graphs
(2023)
Conference Proceeding
Fast parameterized preprocessing for polynomial-time solvable graph problems
(2023)
Journal Article
The complexity of computing optimum labelings for temporal connectivity
(2022)
Conference Proceeding
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search