Given a graph $G$, the optimization version of the graph burning problem seeks for a sequence of vertices, $(u_1,u_2,...,u_k) \in V(G)^k$, with minimum $k$ and such that every $v \in V(G)$ has distance at most $k-i$ to some vertex $u_i$. The length $k$ of the optimal solution is known as the burning number and is denoted by $b(G)$, an invariant that helps quantify the graph's vulnerability to contagion. This paper explores the advantages and limitations of an $\mathcal{O}(mn + kn^2)$ deterministic greedy heuristic for this problem, where $n$ is the graph's order, $m$ is the graph's size, and $k$ is a guess on $b(G)$. This heuristic is based on the relationship between the graph burning problem and the clustered maximum coverage problem, and despite having limitations on paths and cycles, it found most of the optimal and best-known solutions of benchmark and synthetic graphs with up to 102400 vertices.

翻译：给定图$G$，图燃烧问题的优化版本寻求一个顶点序列$(u_1,u_2,...,u_k) \in V(G)^k$，使得$k$最小且每个$v \in V(G)$到某个顶点$u_i$的距离至多为$k-i$。最优解的长度$k$称为燃烧数，记为$b(G)$，该不变量有助于量化图对传染的脆弱性。本文探讨了一种针对该问题的$\mathcal{O}(mn + kn^2)$确定性贪婪启发式算法的优势与局限性，其中$n$为图的阶数，$m$为图的边数，$k$为对$b(G)$的估计值。该启发式基于图燃烧问题与聚类最大覆盖问题之间的关系，尽管在路径和环上存在局限性，但对于顶点数高达102400的基准图和合成图，它找到了大部分最优解和已知最佳解。