Spanners in Hypergraphs

In this work, we explore parallel construction of spanners in hypergraphs. Spanners include all vertices and a subset of edges while ensuring shortest distances within an allowable stretch, making them crucial for efficiently approximating graph structures. We build on the state-of-the-art algorithm for parallel graphs spanners and extend it for hypergraphs focusing on optimizing the size without compromising stretch. We propose techniques to address memory and performance challenges when dealing with hypergraphs.

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Publication Details

Published
2024-12-18
DOI
https://doi.org/10.1109/hipcw63042.2024.00085
Primary Topic
Advanced Graph Neural Networks
Type
article
Field-Weighted Citation Impact
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article

Spanners in Hypergraphs

Yogish Sabharwal, Maulein Pathak, Neelima Gupta
Advanced Graph Neural Networks
article

Spanners in Hypergraphs

Yogish Sabharwal, Maulein Pathak, Neelima Gupta
article en

Abstract

In this work, we explore parallel construction of spanners in hypergraphs. Spanners include all vertices and a subset of edges while ensuring shortest distances within an allowable stretch, making them crucial for efficiently approximating graph structures. We build on the state-of-the-art algorithm for parallel graphs spanners and extend it for hypergraphs focusing on optimizing the size without compromising stretch. We propose techniques to address memory and performance challenges when dealing with hypergraphs.

University of Delhi (IN), IBM Research - India (IN)
Openalex Percentile: Top 75%
Advanced Graph Neural Networks
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