Almost-Linear Time Algorithms for Incremental Graphs: Cycle Detection, SCCs, s-t Shortest Path, and Minimum-Cost Flow

Abstract

We give the first almost-linear time algorithms for several problems in incremental graphs including cycle detection, strongly connected component maintenance, s-t shortest path, maximum flow, and minimum-cost flow. To solve these problems, we give a deterministic data structure that returns a mo(1)-approximate minimum-ratio cycle in fully dynamic graphs in amortized mo(1) time per update. Combining this with the interior point method framework of Brand-Liu-Sidford (STOC 2023) gives the first almost-linear time algorithm for deciding the first update in an incremental graph after which the cost of the minimum-cost flow attains value at most some given threshold F. By rather direct reductions to minimum-cost flow, we are then able to solve the problems in incremental graphs mentioned above. Our new data structure also leads to a modular and deterministic almost-linear time algorithm for minimum-cost flow by removing the need for complicated modeling of a restricted adversary, in contrast to the recent randomized and deterministic algorithms for minimum-cost flow in Chen-Kyng-Liu-Peng-Probst Gutenberg-Sachdeva (FOCS 2022)Brand-Chen-Kyng-Liu-Peng-Probst Gutenberg-Sachdeva-Sidford (FOCS 2023). At a high level, our algorithm dynamizes the ℓ1 oblivious routing of Rozhoň-Grunau-Haeupler-Zuzic-Li (STOC 2022), and develops a method to extract an approximate minimum ratio cycle from the structure of the oblivious routing. To maintain the oblivious routing, we use tools from concurrent work of Kyng-Meierhans-Probst Gutenberg (STOC 2024) which designed vertex sparsifiers for shortest paths, in order to maintain a sparse neighborhood cover in fully dynamic graphs. To find a cycle, we first show that an approximate minimum ratio cycle can be represented as a fundamental cycle on a small set of trees resulting from the oblivious routing. Then, we find a cycle whose quality is comparable to the best tree cycle. This final cycle query step involves vertex and edge sparsification procedures reminiscent of the techniques introduced in Chen-Kyng-Liu-Peng-Probst Gutenberg-Sachdeva (FOCS 2022), but crucially requires a more powerful dynamic spanner, which can handle far more edge insertions than prior work. We build such a spanner via a construction that hearkens back to the classic greedy spanner algorithm of Althöfer-Das-Dobkin-Joseph-Soares (DiscreteComputational Geometry 1993).