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

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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 m^{o(1)}-approximate minimum-ratio cycle in fully dynamic graphs in amortized m^{o(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.
At a high level, our algorithm dynamizes the \ell_1 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 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 previous works, but crucially requires a more powerful dynamic spanner which can handle far more edge insertions. We build such a spanner via a construction that hearkens back to the classic greedy spanner algorithm.