Matchings in Low-Arboricity Graphs in the Dynamic Graph Stream Model

We consider the problem of estimating the size of a maximum matching in low-arboricity graphs in the dynamic graph stream model. In this setting, an algorithm with limited memory makes multiple passes over a stream of edge insertions and deletions, resulting in a low-arboricity graph. Let n be the number of vertices of the input graph, and α be its arboricity. We give the following results. 1) As our main result, we give a three-pass streaming algorithm that produces an (α + 2) (1 + ε) -approximation and uses space O (ε^-2⋅α²⋅n^1/2⋅log n). This result should be contrasted with the Ω (α^-5/2⋅n^1/2) space lower bound established by Assadi et al. , SODA'17 for one-pass algorithms, showing that, for graphs of constant arboricity, the one-pass space lower bound can be achieved in three passes (up to poly-logarithmic factors). Furthermore, we obtain a two-pass algorithm that uses space O (ε^-2⋅α²⋅n^3/5⋅log n). 2) We also give a (1+ε) -approximation multi-pass algorithm, where the space used is parameterized by an upper bound on the size of a largest matching. For example, using O (log log n) passes, the space required is O (ε^-1⋅α²⋅k⋅log n), where k denotes an upper bound on the size of a largest matching. Finally, we define a notion of arboricity in the context of matrices. This is a natural measure of the sparsity of a matrix that is more nuanced than simply bounding the total number of nonzero entries, but less restrictive than bounding the number of nonzero entries in each row and column. For such matrices, we exploit our results on estimating matching size to present upper bounds for the problem of rank estimation in the dynamic data stream model.