Near-Optimal Hypergraph Sparsification in Insertion-Only and Bounded-Deletion Streams

Abstract

We study the problem of constructing hypergraph cut sparsifiers in the streaming model where a hypergraph on n vertices is revealed either via an arbitrary sequence of hyperedge insertions alone (insertion-only streaming model) or via an arbitrary sequence of hyperedge insertions and deletions (dynamic streaming model). For any ε ∈ (0,1), a (1 ± ε) hypergraph cut-sparsifier of a hypergraph H is a reweighted subgraph H' whose cut values approximate those of H to within a (1 ± ε) factor. Prior work shows that in the static setting, one can construct a (1 ± ε) hypergraph cut-sparsifier using Õ(nr/ε²) bits of space [Chen-Khanna-Nagda FOCS 2020], and in the setting of dynamic streams using Õ(nrlog m/ε²) bits of space [Khanna-Putterman-Sudan FOCS 2024]; here the Õ notation hides terms that are polylogarithmic in n, and we use m to denote the total number of hyperedges in the hypergraph. Up until now, the best known space complexity for insertion-only streams has been the same as that for the dynamic streams. This naturally poses the question of understanding the complexity of hypergraph sparsification in insertion-only streams.
Perhaps surprisingly, in this work we show that in insertion-only streams, a (1 ± ε) cut-sparsifier can be computed in Õ(nr/ε²) bits of space, matching the complexity of the static setting. As a consequence, this also establishes an Ω(log m) factor separation between the space complexity of hypergraph cut sparsification in insertion-only streams and dynamic streams, as the latter is provably known to require Ω(nr log m) bits of space. To better explain this gap, we then show a more general result: namely, if the stream has at most k hyperedge deletions then Õ(n r log k/ε²) bits of space suffice for hypergraph cut sparsification. Thus the space complexity smoothly interpolates between the insertion-only regime (k = 0) and the fully dynamic regime (k = m). Our algorithmic results are driven by a key technical insight: once sufficiently many hyperedges have been inserted into the stream (relative to the number of allowed deletions), we can significantly reduce the underlying hypergraph by size by irrevocably contracting large subsets of vertices.
Finally, we complement this result with an essentially matching lower bound of Ω(n r log(k/n)) bits, thus providing essentially a tight characterization of the space complexity for hypergraph cut-sparsification across a spectrum of streaming models.