The Minrank of Random Graphs

TL;DR

This paper establishes tight bounds on the minrank of Erdős-Rényi random graphs, resolving an open problem and impacting index coding, network coding, and circuit complexity.

Contribution

It provides the first tight bounds on minrank for all regimes of p in G(n,p), improving previous bounds and settling the linear index coding problem for random graphs.

Findings

Minrank of G(n,p) is Θ(n/log n) for constant p with high probability

Improves lower bounds from Ω(√n) to Θ(n/log n)

Settles the linear index coding problem for random graphs

Abstract

The minrank of a graph $G$ is the minimum rank of a matrix $M$ that can be obtained from the adjacency matrix of $G$ by switching some ones to zeros (i.e., deleting edges) and then setting all diagonal entries to one. This quantity is closely related to the fundamental information-theoretic problems of (linear) index coding (Bar-Yossef et al., FOCS'06), network coding and distributed storage, and to Valiant's approach for proving superlinear circuit lower bounds (Valiant, Boolean Function Complexity '92). We prove tight bounds on the minrank of random Erd\H{o}s-R\'enyi graphs $G(n,p)$ for all regimes of $p\in[0,1]$. In particular, for any constant $p$, we show that $\mathsf{minrk}(G) = \Theta(n/\log n)$ with high probability, where $G$ is chosen from $G(n,p)$. This bound gives a near quadratic improvement over the previous best lower bound of $\Omega(\sqrt{n})$ (Haviv and Langberg, ISIT'12), and partially settles an open problem raised by Lubetzky and Stav (FOCS '07). Our lower bound matches the well-known upper bound obtained by the "clique covering" solution, and settles the linear index coding problem for random graphs. Finally, our result suggests a new avenue of attack, via derandomization, on Valiant's approach for proving superlinear lower bounds for logarithmic-depth semilinear circuits.