Dynamic Graph Algorithms with Batch Updates in the Massively Parallel Computation Model

Abstract: We study dynamic graph algorithms in the Massively Parallel Computation model, which was inspired by practical data processing systems. Our goal is to provide algorithms that can efficiently handle large batches of edge insertions and deletions. We show algorithms that require fewer rounds to update a solution to problems such as Minimum Spanning Forest, 2-Edge Connected Components, and Maximal Matching than would be required by their static counterparts to compute it from scratch. They work in the most restrictive memory regime, in which local memory per machine is strongly sublinear in the number of graph vertices. Improving on the size of the batch they can handle efficiently would improve on the round complexity of known static algorithms on sparse graphs. Our algorithms can process batches of updates of size $\Theta(S)$, for Minimum Spanning Forest and 2-Edge Connected Components, and $\Theta(S^{1-\varepsilon})$, for Maximal Matching, in $O(1)$ rounds, where $S$ is the local memory of a single machine.