Dynamic Approximate Shortest Paths and Beyond: Subquadratic and Worst-Case Update Time

Consider the following distance query for an $n$-node graph $G$ undergoing edge insertions and deletions: given two sets of nodes $I$ and $J$, return the distances between every pair of nodes in $I\times J$. This query is rather general and captures several versions of the dynamic shortest paths problem. In this paper, we develop an efficient $(1+\epsilon)$-approximation algorithm for this query using fast matrix multiplication. Our algorithm leads to answers for some open problems for Single-Source and All-Pairs Shortest Paths (SSSP and APSP), as well as for Diameter, Radius, and Eccentricities. Below are some highlights. Note that all our algorithms guarantee worst-case update time and are randomized (Monte Carlo), but do not need the oblivious adversary assumption. Subquadratic update time for SSSP, Diameter, Centralities, ect.: When we want to maintain distances from a single node explicitly (without queries), a fundamental question is to beat trivially calling Dijkstra's static algorithm after each update, taking $\Theta(n^2)$ update time on dense graphs. It was known to be improbable for exact algorithms and for combinatorial any-approximation algorithms to polynomially beat the $\Omega(n^2)$ bound (under some conjectures) [Roditty, Zwick, ESA'04; Abboud, V. Williams, FOCS'14]. Our algorithm with $I={s}$ and $J=V(G)$ implies a $(1+\epsilon)$-approximation algorithm for this, guaranteeing $\tilde O(n^{{1.823}/\epsilon2)$} worst-case update time for directed graphs with positive real weights in $[1, W]$. With ideas from [Roditty, V. Williams, STOC'13], we also obtain the first subquadratic worst-case update time for $(5/3+\epsilon)$-approximating the eccentricities and $(1.5+\epsilon)$-approximating the diameter and radius for unweighted graphs (with small additive errors). [...]