Functionality of Random Graphs
John Sylvester, Viktor Zamaraev, Maksim Zhukovskii
TL;DR
This work determines the functionality of random graphs G(n,p) up to a constant factor across the entire range of p. By combining three technical approaches—degeneracy-based bounds, small distinguishing sets, and dominating-set arguments in random bipartite graphs—the authors derive tight upper bounds that peak near p* = √(ln n / n) and match lower bounds in both sparse and dense regimes. The main findings show that fun(G(n,p)) scales as Θ( np p ln(ew)/ln n ) around p = √( ln n /(n w) ) and as Θ( ln(ew)/p ) around p = √( w ln n / n ), with the maximum value Θ(√(n/ln n)) achieved near p*. Functionality thus grows more slowly than many classical width parameters, and the results substantially tighten previous general bounds, revealing a nuanced phase transition in the locality-aware encoding power of random graphs. The paper introduces novel domination bounds in random bipartite graphs and leverages them to bridge subgraph-correctness with global structure, advancing our understanding of adjacency-labeling schemes and graph encodings.
Abstract
The functionality of a graph $G$ is the minimum number $k$ such that in every induced subgraph of $G$ there exists a vertex whose neighbourhood is uniquely determined by the neighborhoods of at most $k$ other vertices in the subgraph. The functionality parameter was introduced in the context of adjacency labeling schemes, and it generalises a number of classical and recent graph parameters including degeneracy, twin-width, and symmetric difference. We establish the functionality of a random graph $G(n,p)$ up to a constant factor for every value of $p$.
