Reduction and Fixed Points of Boolean Networks and Linear Network Coding Solvability

Gadouleau, Maximilien; Richard, Adrien; Fanchon, Eric

Abstract

Linear network coding transmits data through networks by letting the intermediate nodes combine the messages they receive and forward the combinations towards their destinations. The solvability problem asks whether the demands of all the destinations can be simultaneously satisfied by using linear network coding. The guessing number approach converts this problem to determining the number of fixed points of coding functions f : An ! An over a finite alphabet A (usually referred to as Boolean networks if A = f0; 1g) with a given interaction graph, that describes which local functions depend on which variables. In this paper, we generalise the so-called reduction of coding functions in order to eliminate variables. We then determine the maximum number of fixed points of a fully reduced coding function, whose interaction graph has a loop on every vertex. Since the reduction preserves the number of fixed points, we then apply these ideas and results to obtain four main results on the linear network coding solvability problem. First, we prove that non-decreasing coding functions cannot solve any more instances than routing already does. Second, we show that triangle-free undirected graphs are linearly solvable if and only if they are solvable by routing. This is the first classification result for the linear network coding solvability problem. Third, we exhibit a new class of non-linearly solvable graphs. Fourth, we determine large classes of strictly linearly solvable graphs.

Citation

Gadouleau, M., Richard, A., & Fanchon, E. (2016). Reduction and Fixed Points of Boolean Networks and Linear Network Coding Solvability. IEEE Transactions on Information Theory, 62(5), 2504-2519. https://doi.org/10.1109/tit.2016.2544344

• Journal Article Type: Article
• Acceptance Date: Mar 5, 2016
• Online Publication Date: Mar 21, 2016
• Publication Date: May 1, 2016
• Deposit Date: Mar 17, 2016
• Publicly Available Date: Mar 18, 2016
• Journal: IEEE Transactions on Information Theory
• Print ISSN: 0018-9448
• Electronic ISSN: 1557-9654
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 62
• Issue: 5
• Pages: 2504-2519
• DOI: https://doi.org/10.1109/tit.2016.2544344
• Public URL: https://durham-repository.worktribe.com/output/1385949