The d-identifying codes problem for vertex identification in graphs: probabilistic analysis and an approximation algorithm

Authors:
Ying Xiao;Christoforos Hadjicostis;Krishnaiyan Thulasiraman
Affiliations:
Packet Design Inc., Palo Alto, CA;University of Illinois at Urbana-Champaign, IL;University of Oklahoma, Norman, OK
Venue:
COCOON'06 Proceedings of the 12th annual international conference on Computing and Combinatorics
Year:
2006

Citing 6
Cited 0

Diagnosis of t/(t +1)-Diagnosable Systems

SIAM Journal on Computing
New versions of Suen's correlation inequality

proceedings of the eighth international conference on Random structures and algorithms
Optimal codes for strong identification

European Journal of Combinatorics
Diagnosis of t/s-Diagnosable Systems

WG '90 Proceedings of the 16rd International Workshop on Graph-Theoretic Concepts in Computer Science
On the Approximability of the Minimum Test Collection Problem

ESA '01 Proceedings of the 9th Annual European Symposium on Algorithms
On a new class of codes for identifying vertices in graphs

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

Given a graph G(V, E), the identifying codes problem is to find the smallest set of vertices D⊆V such that no two vertices in V are adjacent to the same set of vertices in D. The identifying codes problem has been applied to fault diagnosis and sensor based location detection in harsh environments. In this paper, we introduce and study a generalization of this problem, namely, the d-identifying codes problem. We propose a polynomial time approximation algorithm based on ideas from information theory and establish its approximation ratio that is very close to the best possible. Using analysis on random graphs, several fundamental properties of the optimal solution to this problem are also derived.