Learning a Hidden Hypergraph
Dana Angluin, Jiang Chen; 7(Oct):2215--2236, 2006.
AbstractWe consider the problem of learning a hypergraph using edge-detecting queries. In this model, the learner may query whether a set of vertices induces an edge of the hidden hypergraph or not. We show that an r-uniform hypergraph with m edges and n vertices is learnable with O(24rm · poly(r,logn)) queries with high probability. The queries can be made in O(min(2r (log m+r)2, (log m+r)3)) rounds. We also give an algorithm that learns an almost uniform hypergraph of dimension r using O(2O((1+Δ/2)r) · m1+Δ/2 · poly(log n)) queries with high probability, where Δ is the difference between the maximum and the minimum edge sizes. This upper bound matches our lower bound of Ω((m/(1+Δ/2))1+Δ/2) for this class of hypergraphs in terms of dependence on m. The queries can also be made in O((1+Δ) · min(2r (log m+r)2, (log m+r)3)) rounds.