On the Streaming Complexity of Expander Decomposition

In this paper we study the problem of finding (ϵ, ϕ)-expander decompositions of a graph in the streaming model, in particular for dynamic streams of edge insertions and deletions. The goal is to partition the vertex set so that every component induces a ϕ-expander, while the number of inter-cluster edges is only an ϵ fraction of the total volume. It was recently shown that there exists a simple algorithm to construct a (O(ϕlog n), ϕ)-expander decomposition of an n-vertex graph using Oe(n/ϕ2) bits of space [Filtser, Kapralov, Makarov, ITCS’23]. This result calls for understanding the extent to which a dependence in space on the sparsity parameter ϕ is inherent. We move towards answering this question on two fronts. We prove that a (O(ϕlog n), ϕ)-expander decomposition can be found using Oe(n) space, for every ϕ. At the core of our result is the first streaming algorithm for computing boundary-linked expander decompositions, a recently introduced strengthening of the classical notion [Goranci et al., SODA’21]. The key advantage is that a classical sparsifier [Fung et al., STOC’11], with size independent of ϕ, preserves the cuts inside the clusters of a boundary-linked expander decomposition within a multiplicative error. Notable algorithmic applications use sequences of expander decompositions, in particular one often repeatedly computes a decomposition of the subgraph induced by the inter-cluster edges (e.g., the seminal work of Spielman and Teng on spectral sparsifiers [Spielman, Teng, SIAM Journal of Computing 40(4)], or the recent maximum flow breakthrough [Chen et al., FOCS’22], among others). We prove that any streaming algorithm that computes a sequence of (O(ϕlog n), ϕ)-expander decompositions requires Ω(e n/ϕ) bits of space, even in insertion only streams.