Join Sampling Under Acyclic Degree Constraints and (Cyclic) Subgraph Sampling

Given a (natural) join with an acyclic set of degree constraints (the join itself does not need to be acyclic), we show how to draw a uniformly random sample from the join result in O(polymat/max{1, OUT}) expected time (assuming data complexity) after a preprocessing phase of O(IN) expected time, where IN, OUT, and polymat are the join’s input size, output size, and polymatroid bound, respectively. This compares favorably with the state of the art (Deng et al. and Kim et al., both in PODS'23), which states that, in the absence of degree constraints, a uniformly random sample can be drawn in Õ(AGM/max{1, OUT}) expected time after a preprocessing phase of Õ(IN) expected time, where AGM is the join’s AGM bound and Õ(.) hides a polylog(IN) factor. Our algorithm applies to every join supported by the solutions of Deng et al. and Kim et al. Furthermore, since the polymatroid bound is at most the AGM bound, our performance guarantees are never worse, but can be considerably better, than those of Deng et al. and Kim et al. We then utilize our techniques to tackle directed subgraph sampling, a problem that has extensive database applications and bears close relevance to joins. Let G = (V, E) be a directed data graph where each vertex has an out-degree at most λ, and let P be a directed pattern graph with a constant number of vertices. The objective is to uniformly sample an occurrence of P in G. The problem can be modeled as join sampling with input size IN = Θ(|E|) but, whenever P contains cycles, the converted join has cyclic degree constraints. We show that it is always possible to throw away certain degree constraints such that (i) the remaining constraints are acyclic and (ii) the new join has asymptotically the same polymatroid bound polymat as the old one. Combining this finding with our new join sampling solution yields an algorithm to sample from the original (cyclic) join (thereby yielding a uniformly random occurrence of P) in O(polymat/max{1, OUT}) expected time after O(|E|) expected-time preprocessing, where OUT is the number of occurrences.