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Unlike the sum in the usual shortest path problem, the bottleneck cost of a nonempty graph path is its largest edge weight. The optimum is the smallest threshold at which the endpoints become connected by edges of weight at most that threshold. Sorting edges and increasing the threshold, with a disjoint-set data structure maintaining components, finds this value. The minimax path property of a minimum spanning tree provides a single tree representing such optimal routes for every pair of vertices.

Take a minimum spanning tree $T$, for example one constructed by Kruskal's algorithm. Fix $v$ and let $e$ be a longest edge of the tree's unique $xv$ path. Deleting $e$ partitions $T$ into two components separating $x$ and $v$. Any alternative $xv$ path crosses this graph cut. If all of its edges were shorter than $e$, a crossing edge $f$ would let us replace $e$ by $f$, producing a spanning tree of smaller total length. This contradicts minimality of $T$.

Therefore every competing path has an edge at least as long as $e$, and the tree path solves the bottleneck shortest path problem. The same minimum spanning tree works simultaneously for every $v$; this is the minimax path property of a minimum spanning tree.