Output-sensitive approximate counting via a measure-bounded hyperedge oracle, or: How asymmetry helps estimate $k$-clique counts faster
Keren Censor-Hillel, Tomer Even, Virginia Vassilevska Williams
TL;DR
This work extends the fine-grained reduction from approximate counting to decision to an output-sensitive regime by introducing a measure-bound hyperedge oracle. By defining μ(U) = ∏_{i=1}^k |U∩V_i| and restricting oracle queries to low-measure subgraphs, the authors obtain polylogarithmic-query reductions that yield (1±ε)-approximate counts for hyperedge counts, including k-clique, k-dominating set, and k-sum, with runtimes governed by matrix-multiplication exponents. The core framework combines a recursive heavy/light vertex decomposition with a measure-aware detection oracle, enabling significant improvements in the output-sensitive regime and linking approximation to detection through a generalized “Duplicatable” class of problems. The results provide near-optimal running times in several regimes (e.g., for large k-t, m = n^t cliques, etc.) and establish a versatile methodology for turning detection oracles into fast approximate counting oracles for a broad family of combinatorial counting problems. Practically, this advances exact or near-exact counts in sublinear-regime or with runtimes closely tied to the witness count, offering tighter trade-offs in fine-grained complexity contexts.
Abstract
Dell, Lapinskas and Meeks [DLM SICOMP 2022] presented a general reduction from approximate counting to decision for a class of fine-grained problems that can be viewed as hyperedge counting or detection problems in an implicit hypergraph, thus obtaining tight equivalences between approximate counting and decision for many key problems such as $k$-clique, $k$-sum and more. Their result is a reduction from approximately counting the number of hyperedges in an implicit $k$-partite hypergraph to a polylogarithmic number of calls to a hyperedge oracle that returns whether a given subhypergraph contains an edge. The main result of this paper is a generalization of the DLM result for {\em output-sensitive} approximate counting, where the running time of the desired counting algorithm is inversely proportional to the number of witnesses. Our theorem is a reduction from approximately counting the (unknown) number of hyperedges in an implicit $k$-partite hypergraph to a polylogarithmic number of calls to a hyperedge oracle called only on subhypergraphs with a small ``measure''. If a subhypergraph has $u_i$ nodes in the $i$th node partition of the $k$-partite hypergraph, then its measure is $\prod_i u_i$. Using the new general reduction and by efficiently implementing measure-bounded colorful independence oracles, we obtain new improved output-sensitive approximate counting algorithms for $k$-clique, $k$-dominating set and $k$-sum. In graphs with $n^t$ $k$-cliques, for instance, our algorithm $(1\pm ε)$-approximates the $k$-clique count in time $$\tilde{O}_ε(n^{ω(\frac{k-t-1}{3},\frac{k-t}{3},\frac{k-t+2}{3}) }+n^2),$$ where $ω(a,b,c)$ is the exponent of $n^a\times n^b$ by $n^b\times n^c$ matrix multiplication. For large $k$ and $t>2$, this is a substantial improvement over prior work, even if $ω=2$.