Stochastic Embedding of Digraphs into DAGs

Given a weighted digraph $G=(V,E,w)$, a stochastic embedding into DAGs is a distribution $\mathcal{D}$ over pairs of DAGs $(D_1,D_2)$ such that for every $u,v$: (1) the reachability is preserved: $u\rightsquigarrow_G v$ (i.e., $v$ is reachable from $u$ in $G$) implies that $u\rightsquigarrow_{D_1} v$ or $u\rightsquigarrow_{D_2} v$ (but not both), and (2) distances are dominated: $d_G(u,v)\le\min\{d_{D_1}(u,v),d_{D_2}(u,v)\}$. The stochastic embedding $\mathcal{D}$ has expected distortion $t$ if for every $u,v\in V$, $$\mathbb{E}_{(D_{1},D_{2})\sim\mathcal{D}}\left[d_{D_{1}}(u,v)\cdot\boldsymbol{1}[u\rightsquigarrow_{D_{1}}v]+d_{D_{2}}(u,v)\cdot\boldsymbol{1}[u\rightsquigarrow_{D_{2}}v]\right]\le t\cdot d_{G}(u,v)~.$$ Finally, the sparsity of $\mathcal{D}$ is the maximum number of edges in any of the DAGs in its support. Given an $n$ vertex digraph with $m$ edges, we construct a stochastic embedding into DAGs with expected distortion $\tilde{O}(\log n)$ and $\tilde{O}(m)$ sparsity, improving a previous result by Assadi, Hoppenworth, and Wein [STOC 25], which achieved expected distortion $\tilde{O}(\log^3 n)$. Further, we can sample DAGs from this distribution in $\tilde{O}(m)$ time.