theoretical computer science

Exploiting Graph Structure for Near-Optimal Broadcasting

arXiv:2607.14032

summary

The paper studies faster approximation algorithms for the graph broadcasting problem, providing additive‑approximation schemes and improved exact algorithms while also showing parameterized hardness for several graph parameters.

Abstract

Telephone broadcasting is a classical model for spreading information in a network. Given a connected graph with source vertex , each informed vertex may inform exactly one uninformed neighbor in every time step. The \textsc{Broadcasting} problem asks whether all vertices can be informed within steps; the minimum such value is the broadcast time . A related variant considers the worst-case source, . Both variants are NP-hard, and every -vertex graph satisfies . Fomin \textit{et al.}~\cite{fomin2023parameterized} recently gave FPT algorithms for this problem under several structural graph parameters. Instead of computing optimal broadcast schedules, we study faster approximation algorithms that produce valid schedules. We improve the exact algorithm of Fomin \textit{et al.} to an algorithm with a additive approximation, where is a constant for every fixed . We also give approximation algorithms on graphs of bounded vertex integrity, including a polynomial-time additive approximation algorithm. Complementing these positive results, we prove parameterized hardness for vertex cover above maximum matching (), dominating set size, and graph diameter, indicating that FPT algorithms for these parameters are unlikely. Finally, we present a additive approximation algorithm for distance-to-clique running in time, a -factor approximation algorithm for distance-to-path running in XP time, and a polynomial-time algorithm for polar graphs.

Topics & keywords

#graph algorithms#broadcasting#approximation algorithms#parameterized complexity#graph parametersbroadcast timeadditive approximationvertex integrityFPT algorithmsparameterized hardnessdistance-to-clique
Exploiting Graph Structure for Near-Optimal Broadcasting · wovepaper