The Densest SWAMP problem: subhypergraphs with arbitrary monotonic partial edge rewards

Fuente: arXiv
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Autores principales: Bengali, Vedangi, Tatti, Nikolaj, Kumpulainen, Iiro, Adriaens, Florian, Veldt, Nate
Formato: Preprint
Publicado: 2025
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author Bengali, Vedangi
Tatti, Nikolaj
Kumpulainen, Iiro
Adriaens, Florian
Veldt, Nate
author_facet Bengali, Vedangi
Tatti, Nikolaj
Kumpulainen, Iiro
Adriaens, Florian
Veldt, Nate
contents We consider a generalization of the densest subhypergraph problem where nonnegative rewards are given for including partial hyperedges in a dense subhypergraph. Prior work addressed this problem only in cases where reward functions are convex, in which case the problem is poly-time solvable. We consider a broader setting where rewards are monotonic but otherwise arbitrary. We first prove hardness results for a wide class of non-convex rewards, then design a 1/k-approximation by projecting to the nearest set of convex rewards, where k is the maximum hyperedge size. We also design another 1/k-approximation using a faster peeling algorithm, which (somewhat surprisingly) differs from the standard greedy peeling algorithm used to approximate other variants of the densest subgraph problem. Our results include an empirical analysis of our algorithm across several real-world hypergraphs.
format Preprint
id arxiv_https___arxiv_org_abs_2506_12998
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle The Densest SWAMP problem: subhypergraphs with arbitrary monotonic partial edge rewards
Bengali, Vedangi
Tatti, Nikolaj
Kumpulainen, Iiro
Adriaens, Florian
Veldt, Nate
Data Structures and Algorithms
We consider a generalization of the densest subhypergraph problem where nonnegative rewards are given for including partial hyperedges in a dense subhypergraph. Prior work addressed this problem only in cases where reward functions are convex, in which case the problem is poly-time solvable. We consider a broader setting where rewards are monotonic but otherwise arbitrary. We first prove hardness results for a wide class of non-convex rewards, then design a 1/k-approximation by projecting to the nearest set of convex rewards, where k is the maximum hyperedge size. We also design another 1/k-approximation using a faster peeling algorithm, which (somewhat surprisingly) differs from the standard greedy peeling algorithm used to approximate other variants of the densest subgraph problem. Our results include an empirical analysis of our algorithm across several real-world hypergraphs.
title The Densest SWAMP problem: subhypergraphs with arbitrary monotonic partial edge rewards
topic Data Structures and Algorithms
url https://arxiv.org/abs/2506.12998