APX-Hardness and Approximation for the k-Burning Number Problem

TL;DR

This paper studies the computational complexity of the k-burning number problem, proving APX-hardness for fixed k, providing a 3-approximation algorithm, and showing NP-hardness of finding the burning sequence.

Contribution

It establishes the APX-hardness of computing the k-burning number for fixed k, introduces an efficient approximation algorithm, and proves the NP-hardness of determining the burning sequence.

Findings

Computing k-burning number is APX-hard for any fixed k.

A 3-approximation algorithm runs in O((n+m) log n) time.

Finding the burning sequence is NP-hard even with sources given.

Abstract

Consider an information diffusion process on a graph $G$ that starts with $k>0$ burnt vertices, and at each subsequent step, burns the neighbors of the currently burnt vertices, as well as $k$ other unburnt vertices. The \emph{$k$-burning number} of $G$ is the minimum number of steps $b_k(G)$ such that all the vertices can be burned within $b_k(G)$ steps. Note that the last step may have smaller than $k$ unburnt vertices available, where all of them are burned. The $1$-burning number coincides with the well-known burning number problem, which was proposed to model the spread of social contagion. The generalization to $k$-burning number allows us to examine different worst-case contagion scenarios by varying the spread factor $k$. In this paper we prove that computing $k$-burning number is APX-hard, for any fixed constant $k$. We then give an $O((n+m)\log n)$-time 3-approximation algorithm for computing $k$-burning number, for any $k\ge 1$, where $n$ and $m$ are the number of vertices and edges, respectively. Finally, we show that even if the burning sources are given as an input, computing a burning sequence itself is an NP-hard problem.