Near-optimal Linear Sketches and Fully-Dynamic Algorithms for Hypergraph Spectral Sparsification

A hypergraph spectral sparsifier of a hypergraph $G$ is a weighted subgraph $H$ that approximates the Laplacian of $G$ to a specified precision. Recent work has shown that similar to ordinary graphs, there exist $\tilde{O}(n)$-size hypergraph spectral sparsifiers. However, the task of computing such sparsifiers turns out to be much more involved, and all known algorithms rely on the notion of balanced weight assignments, whose computation inherently relies on repeated, complete access to the underlying hypergraph. We introduce a significantly simpler framework for hypergraph spectral sparsification which bypasses the need to compute such weight assignments, essentially reducing hypergraph sparsification to repeated effective resistance sampling in $\mathit{\text{ordinary}}\mathit{\text{graphs}}$, which are obtained by $\mathit{\text{oblivious}}\mathit{\text{vertex-sampling}}$ of the original hypergraph. Our framework immediately yields a simple, new nearly-linear time algorithm for nearly-linear size spectral hypergraph sparsification. Furthermore, as a direct consequence of our framework, we obtain the first nearly-optimal algorithms in several other models of computation, namely the linear sketching, fully dynamic, and online settings.