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Graph Guessing Games and Non-Shannon Information Inequalities.

Authors :
Baber, Rahil
Dang, Anh N.
Vaughan, Emil R.
Riis, Soren
Christofides, Demetres
Source :
IEEE Transactions on Information Theory. Jul2017, Vol. 63 Issue 7, p4257-4267. 11p.
Publication Year :
2017

Abstract

Guessing games for directed graphs were introduced by Riis for studying multiple unicast network coding problems. In a guessing game, the players toss generalised dice and can see some of the other outcomes depending on the structure of an underlying digraph. They later guess simultaneously the outcome of their own die. Their objective is to find a strategy, which maximizes the probability that they all guess correctly. The performance of the optimal strategy for a graph is measured by the guessing number of the digraph. Christofides and Markström studied guessing numbers of undirected graphs and defined a strategy which they conjectured to be optimal. One of the main results of this paper is a disproof of this conjecture. The main tool so far for computing guessing numbers of graphs is information theoretic inequalities. The other main result of this paper is that Shannon’s information inequalities, which work particularly well for a wide range of graph classes, are not sufficient for computing the guessing number. Finally, we pose a few more interesting questions some of which we can answer and some which we leave as open problems. [ABSTRACT FROM PUBLISHER]

Details

Language :
English
ISSN :
00189448
Volume :
63
Issue :
7
Database :
Academic Search Index
Journal :
IEEE Transactions on Information Theory
Publication Type :
Academic Journal
Accession number :
123685210
Full Text :
https://doi.org/10.1109/TIT.2016.2628819