Optimal Lower Bounds for Distributed and Streaming Spanning Forest Computation
Jelani Nelson, Huacheng Yu · Society for Industrial and Applied Mathematics eBooks · 2019
We show optimal lower bounds for spanning forest computation in two different models: One wants a data structure for fully dynamic spanning forest in which updates can insert or delete edges amongst a base set of n vertices. The sole allowed query asks for a spanning forest, which the data structure should successfully answer with some given (potentially small) constant probability ∊ > 0. We prove that any such data structure must use Ω(n log3 n) bits of memory. There is a referee and n vertices in a network sharing public randomness, and each vertex knows only its neighborhood; the referee receives no input. The vertices each send a message to the referee who then computes a spanning forest of the graph with constant probability ∊ > 0. We prove the average message length must be Ω(log3 n) bits. Both our lower bounds are optimal, with matching upper bounds provided by the AGM sketch [AGM12] (which even succeeds with probability 1 – 1/poly(n)). Furthermore, for the first setting we show optimal lower bounds even for low failure probability δ, as long as δ > 2−n1−∊.