Achievability of Heterogeneous Hypergraph Recovery from its Graph Projection

Fuente: arXiv
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Main Authors: Morgan, Alexander, Guo, Chenghao
Format: Preprint
Published: 2026
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author Morgan, Alexander
Guo, Chenghao
author_facet Morgan, Alexander
Guo, Chenghao
contents We formulate and analyze a heterogeneous random hypergraph model, and we provide an achieveability result for recovery of hyperedges from the observed projected graph. We observe a projected graph which combines random hyperedges across all degrees, where a projected edge appears if and only if both vertices appear in at least one hyperedge. Our goal is to reconstruct the original set of hyperedges of degree $d_j$ for some $j$. Our achievability result is based on the idea of selecting maximal cliques of size $d_j$ in the projected graph, and we show that this algorithm succeeds under a natural condition on the densities. This achievability condition generalizes a known threshold for $d$-uniform hypergraphs with noiseless and noisy projections. We conjecture the threshold to be optimal for recovering hyperedges with the largest degree.
format Preprint
id arxiv_https___arxiv_org_abs_2603_01268
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Achievability of Heterogeneous Hypergraph Recovery from its Graph Projection
Morgan, Alexander
Guo, Chenghao
Data Structures and Algorithms
Information Theory
Probability
Statistics Theory
We formulate and analyze a heterogeneous random hypergraph model, and we provide an achieveability result for recovery of hyperedges from the observed projected graph. We observe a projected graph which combines random hyperedges across all degrees, where a projected edge appears if and only if both vertices appear in at least one hyperedge. Our goal is to reconstruct the original set of hyperedges of degree $d_j$ for some $j$. Our achievability result is based on the idea of selecting maximal cliques of size $d_j$ in the projected graph, and we show that this algorithm succeeds under a natural condition on the densities. This achievability condition generalizes a known threshold for $d$-uniform hypergraphs with noiseless and noisy projections. We conjecture the threshold to be optimal for recovering hyperedges with the largest degree.
title Achievability of Heterogeneous Hypergraph Recovery from its Graph Projection
topic Data Structures and Algorithms
Information Theory
Probability
Statistics Theory
url https://arxiv.org/abs/2603.01268