Reduced Functional Dependence Graph and Its Applications

TL;DR

This paper introduces a reduced functional dependence graph (FDG) that retains essential edges to simplify calculations while preserving capacity bounds, improving computational efficiency in network coding analysis.

Contribution

The paper proposes the concept of reduced FDG, which simplifies the graph by keeping only essential edges, maintaining accuracy while reducing computational complexity.

Findings

Reduced FDG preserves capacity bounds.

Significantly lowers computational complexity.

Applicable in algebraic scalar linear network coding.

Abstract

Functional dependence graph (FDG) is an important class of directed graph that captures the dominance relationship among a set of variables. FDG is frequently used in calculating network coding capacity bounds. However, the order of FDG is usually much larger than the original network and the computational complexity of many bounds grows exponentially with the order of FDG. In this paper, we introduce the concept of reduced FDG, which is obtained from the original FDG by keeping only those "essential" edges. It is proved that the reduced FDG gives the same capacity region/bounds with the original FDG, but requiring much less computation. The applications of reduced FDG in the algebraic formulation of scalar linear network coding is also discussed.