Skip to main contentSkip to search
Episciences
Open Access Journals
Sign in(new window)
Discrete Mathematics & Theoretical Computer Science logo
Discrete Mathematics & Theoretical Computer Science
Discrete Mathematics & Theoretical Computer Science logo
Discrete Mathematics & Theoretical Computer Science
Sign in(new window)
Articles & Issues
All articlesAll accepted articlesAll volumesLast volumeSectionsSpecial issuesAuthors
About
The journalIndexing
Boards
Publish
For authorsEthical charter
Submit
Discrete Mathematics & Theoretical Computer Science logo
Contact
|
Credits
eISSN 1365-8050
|
RSS
|
Atom
Episciences
Documentation
|
Acknowledgements
|
Publishing policy
Accessibility: non-compliant
|
Legal mentions
|
Privacy statement
|
Terms of use
  1. Home > Articles & Issues >
  2. Articles >
  3. Non-adaptive Group T ...
Article

Non-adaptive Group Testing on Graphs

Hamid Kameli
Download article
Open on arXiv
Submitted on
May 3, 2017
Accepted on
January 25, 2018
Published on
March 26, 2018
Last modified on
March 31, 2025
Volume 20
Vol. 20:1
Combinatorics
DOI
10.23638/DMTCS-20-1-9
License
arXiv.org - Non-exclusive license to distribute
Indicators
1115
Views
499
Downloads

Non-adaptive Group Testing on Graphs

Hamid Kameli
Abstract
Grebinski and Kucherov (1998) and Alon et al. (2004-2005) study the problem of learning a hidden graph for some especial cases, such as hamiltonian cycle, cliques, stars, and matchings. This problem is motivated by problems in chemical reactions, molecular biology and genome sequencing. In this paper, we present a generalization of this problem. Precisely, we consider a graph G and a subgraph H of G and we assume that G contains exactly one defective subgraph isomorphic to H. The goal is to find the defective subgraph by testing whether an induced subgraph contains an edge of the defective subgraph, with the minimum number of tests. We present an upper bound for the number of tests to find the defective subgraph by using the symmetric and high probability variation of Lov\'asz Local Lemma.
Keywords
  • Computer Science - Data Structures and Algorithms
  • Computer Science - Machine Learning
Preview
Loading PDF preview...