Vertex Ordering Algorithms for Graph Coloring Problem
August 26, 2020 Β· Declared Dead Β· π Signal Processing and Communications Applications Conference
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Arda Asik, Ibrahim Bugra Demir, Berker Demirel, Baris Batuhan Topal, Kamer Kaya
arXiv ID
2008.11454
Category
cs.DS: Data Structures & Algorithms
Citations
0
Venue
Signal Processing and Communications Applications Conference
Last Checked
5 months ago
Abstract
Graph coloring is a fundamental problem in combinatorics with many applications in practice. In this problem, the vertices in a given graph must be colored by using the least number of colors in such a way that a vertex has a different color than its neighbors. The problem, as well as its different variants, has been proven to be NP-Hard. Therefore, there are greedy algorithms in the literature aiming to use a small number of colors. These algorithms traverse the vertices and color them one by one. The vertex visit order has a significant impact on the number of colors used. In this work, we investigated if social network analytics metrics can be used to find this order. Our experiments showed that when closeness centrality is used to find vertex visit order, a smaller number of colors is used by the greedy algorithms.
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