# Adaptive Learning a Hidden Hypergraph

arXiv: https://arxiv.org/abs/1607.00507 · HTML: https://mlsanity.com/paper/1607.00507/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2016.

## Built on by (most cited first, 1 of 1)

- [Adaptive Sparse M"obius Transforms for Learning Polynomials](https://mlsanity.com/paper/2602.06246/) (2026)

## Abstract (arXiv)

Learning a hidden hypergraph is a natural generalization of the classical group testing problem that consists in detecting unknown hypergraph Hun=H(V,E) by carrying out edge-detecting tests. In the given paper we focus our attention only on a specific family F(t,s,ℓ) of localized hypergraphs for which the total number of vertices |V| = t, the number of edges |E|≤ s, sll t, and the cardinality of any edge |e|≤ℓ, ℓll t. Our goal is to identify all edges of Hun∈ F(t,s,ℓ) by using the minimal number of tests. We provide an adaptive algorithm that matches the information theory bound, i.e., the total number of tests of the algorithm in the worst case is at most sℓlog2 t(1+o(1)).